class MLPModel(Model):
def __init__(self,
ob_space,
ac_space,
ob_filter=True,
gaussian_fixed_var=True):
self.ob_filter = ob_filter
self.gaussian_fixed_var = gaussian_fixed_var
super(MLPModel, self).__init__(ob_space, ac_space)
def _create_network(self):
x = self.ob
# create ob filter
if self.ob_filter:
self.ob_rms = RunningMeanStd(shape=self.ob_space.shape)
x = tf.clip_by_value(
(self.ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
# actor
l = x
l = tf.nn.tanh(
U.dense(l, 32, "a_1", weight_init=U.normc_initializer(1.0)))
l = tf.nn.tanh(
U.dense(l, 32, "a_2", weight_init=U.normc_initializer(1.0)))
action_layer = l
# critic
l = x
l = tf.nn.tanh(
U.dense(l, 32, "c_1", weight_init=U.normc_initializer(1.0)))
l = tf.nn.tanh(
U.dense(l, 32, "c_2", weight_init=U.normc_initializer(1.0)))
value_layer = l
self._create_logit_value(action_layer, value_layer,
self.gaussian_fixed_var)
def update_ob_norm(self, ob):
if not hasattr(self, 'ob_rms'): return
self.ob_rms.update(ob)
class CnnPolicy(object):
recurrent = False
def __init__(self, name, ob_space, ac_space, hid_size, num_hid_layers, kind='large'):
with tf.variable_scope(name):
self._init(ob_space, ac_space, hid_size, num_hid_layers, kind)
self.scope = tf.get_variable_scope().name
self.recurrent = False
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, kind):
assert isinstance(ob_space, tuple)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob_p = U.get_placeholder(name="ob_physics", dtype=tf.float32, shape=[sequence_length] + list(ob_space[0].shape))
ob_f= U.get_placeholder(name="ob_frames", dtype=tf.float32, shape=[sequence_length]+list(ob_space[1].shape))
self.ob = [ob_p, ob_f]
#process ob_p
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape = ob_space[0].shape)
obpz = tf.clip_by_value((ob_p - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
#process ob_f
x = ob_f / 255.0
x = self.img_encoder(x, kind)
ob_last = tf.concat((obpz, x), axis=-1)
with tf.variable_scope("vf"):
last_out = ob_last
for i in range(num_hid_layers):
last_out = tf.nn.relu(tf.layers.dense(last_out, hid_size, name="fc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
self.vpred_ext = tf.layers.dense(last_out, 1, name='vf_ext', kernel_initializer=U.normc_initializer(1.0))[:,0]
self.vpred_int = tf.layers.dense(last_out, 1, name='vf_int', kernel_initializer=U.normc_initializer(1.0))[:,0]
with tf.variable_scope("pol"):
last_out = ob_last
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name='fc%i'%(i+1), kernel_initializer=U.normc_initializer(1.0)))
logits = tf.layers.dense(last_out, pdtype.param_shape()[0], name='logits', kernel_initializer=U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(logits)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob_p, ob_f], [ac, self.vpred_ext, self.vpred_int])
def img_encoder(self, x, kind):
if kind == 'small': # from A3C paper
x = tf.nn.relu(U.conv2d(x, 16, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 32, "l2", [4, 4], [2, 2], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(tf.layers.dense(x, 256, name='lin', kernel_initializer=U.normc_initializer(1.0)))
elif kind == 'large': # Nature DQN
x = tf.nn.relu(U.conv2d(x, 32, "l1", [8, 8], [4, 4], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l2", [4, 4], [2, 2], pad="VALID"))
x = tf.nn.relu(U.conv2d(x, 64, "l3", [3, 3], [1, 1], pad="VALID"))
x = U.flattenallbut0(x)
x = tf.nn.relu(tf.layers.dense(x, 512, name='lin', kernel_initializer=U.normc_initializer(1.0)))
else:
raise NotImplementedError
return x
def act(self, stochastic, ob):
ob1, ob2 = ob
ob2 = np.array(ob2)
ac1, vpred_ext, vpred_int = self._act(stochastic, ob1, ob2)
norm_ac1 = np.tanh(ac1)
return norm_ac1[0], ac1[0], vpred_ext[0], vpred_int[0]
def get_variables(self):
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope)
def get_trainable_variables(self):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)
def get_initial_state(self):
return []
def update_obs_rms(self, ob):
obp = np.array(zip(*ob.tolist())[0])
self.ob_rms.update(obp)
class EnsembleDDPG(object):
def __init__(self, actor, critics, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
gamma=0.99, tau=0.005, normalize_returns=False, enable_popart=False, normalize_observations=False,
batch_size=100, observation_range=(-np.inf, np.inf), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1, action_noise_scale=0.2,
action_noise_clip=0.5,
critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1., use_mpi_adam=False):
# Inputs.
self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
# settings
self.use_mpi_adam = use_mpi_adam
# set the list of critic
self.critics = critics
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# remember the noise scale and clip
self.action_noise_scale = action_noise_scale
self.action_noise_clip = action_noise_clip
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
# set up different target critics, primary and supplementary
target_critics = []
for critic in critics:
target_critic = copy(critic)
target_critic.name = 'target_' + critic.name
target_critics.append(target_critic)
self.target_critics = target_critics
# Create networks and core TF parts that are shared across setup parts.
# actor_tf pi(s) is built from the actor and normalized_obs0
self.actor_tf = actor(normalized_obs0)
# normalized_critic_tf normalized Q(s,a) is built from the observation and action
self.normalized_critic_tfs = [critic(normalized_obs0, self.actions) for critic in critics]
self.normalized_critic_tf_main = self.normalized_critic_tfs[0]
# critic_tf Q(s,a) is built from de-normalization and clipping from normalized Q(s,a)
self.critic_tfs = [ denormalize(tf.clip_by_value(normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
for normalized_critic_tf in self.normalized_critic_tfs]
self.critic_tf_mean = 0
for critic_tf in self.critic_tfs:
self.critic_tf_mean += critic_tf
self.critic_tf_mean *= 1.0 / len(self.critic_tfs)
# normalized_critic_with_actor_tf normalized Q(s,pi(s)) is built from the observation,
# and action provided by actor
self.normalized_critic_with_actor_tfs = [
critic(normalized_obs0, self.actor_tf, reuse=True) for critic in critics
]
# critic_with_actor_tf is built from de-normalization and clipping from normalized Q(s,pi(s))
self.critic_with_actor_tfs = [denormalize(
tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]),
self.ret_rms)
for normalized_critic_with_actor_tf in self.normalized_critic_with_actor_tfs
]
self.critic_with_actor_tf_main = self.critic_with_actor_tfs[0]
self.critic_with_actor_tf_mean = 0
for critic_with_actor_tf in self.critic_with_actor_tfs:
self.critic_with_actor_tf_mean += critic_with_actor_tf
self.critic_with_actor_tf_mean *= 1.0 / len(self.critics)
# Q_obs1 Q(s',pi'(s)) is built from next state s'(observation), target actor pi',
# and de-normalization
target_action = target_actor(normalized_obs1)
self.target_Q_vals = []
self.target_Q_val_mean = 0
# add noise in target critic functions
for target_critic in target_critics:
target_action_noise = tf.clip_by_value(tf.random_normal(
tf.shape(target_action), mean=0.0, stddev=action_noise_scale, dtype=tf.float32),
clip_value_min=-action_noise_clip, clip_value_max=action_noise_clip)
noisy_target_action = tf.clip_by_value(target_action + target_action_noise,
clip_value_min=action_range[0], clip_value_max=action_range[1])
target_Q_obs = denormalize(target_critic(normalized_obs1, noisy_target_action), self.ret_rms)
target_Q_val = self.rewards + (1. - self.terminals1) * gamma * target_Q_obs
self.target_Q_vals.append(target_Q_val)
self.target_Q_val_mean += target_Q_val
self.target_Q_val_mean *= 1.0 / (len(critics))
# merge trainable variables into one set
self.target_critic_vars = []
self.critic_vars = []
self.critic_trainable_vars = []
for critic in critics:
self.critic_vars += critic.vars
self.critic_trainable_vars += critic.trainable_vars
for target_critic in target_critics:
self.target_critic_vars += target_critic.vars
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
# setup optimizer
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
if self.use_mpi_adam:
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars,
self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic_vars, self.target_critic_vars,
self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
self.target_soft_update_actor = actor_soft_updates
self.target_soft_update_critic = critic_soft_updates
else:
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.trainable_vars,
self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic_trainable_vars,
self.target_critic_vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
self.target_soft_update_actor = actor_soft_updates
self.target_soft_update_critic = critic_soft_updates
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
# Here use the Q(s,pi(s)) as the loss function
# use primary critic function to generate policy updates
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf_mean)
self.actor_loss_array = []
for critic_with_actor_tf in self.critic_with_actor_tfs:
self.actor_loss_array.append(-tf.reduce_mean(critic_with_actor_tf))
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = []
for actor_loss in self.actor_loss_array:
self.actor_grads.append(tf.reshape(
U.flatgrad(actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm),
shape=[-1,1]))
self.actor_grad_array = tf.concat(self.actor_grads,axis=1)
self.actor_grad_array = tf.reshape(self.actor_grad_array, shape=[-1, len(self.critics)])
self.actor_grad_mean = tf.reduce_mean(self.actor_grad_array, axis=1)
self.actor_grad_var = reduce_var(self.actor_grad_array, axis=1)
# sum up the gradients
self.actor_grad_var_std = tf.sqrt(tf.reduce_sum(self.actor_grad_var))
# print the shape of gradients
print("[Tiancheng Shape] Actor Grad Array", self.actor_grad_array.shape)
print("[Tiancheng Shape] Actor Grad Mean", self.actor_grad_mean.shape)
print("[Tiancheng Shape] Actor Grad Variance", self.actor_grad_var.shape)
print("[Tiancheng Shape] Actor Grad VarStd", self.actor_grad_var_std.shape)
# add support to single-threaded adam
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
if self.use_mpi_adam:
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
else:
self.actor_grads = list(
zip(tf.gradients(self.actor_loss, self.actor.trainable_vars), self.actor.trainable_vars))
self.actor_optimizer = tf.train.AdamOptimizer(learning_rate=self.actor_lr,beta1=0.9, beta2=0.999, epsilon=1e-08)
self.actor_train = self.actor_optimizer.apply_gradients(self.actor_grads)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
# normalize critic target, normalized y ( not sure we need to use different target values here. )
# TODO: abandon static evaluation of critic target values, use dynamic computing method
# Use square error between normalized_critic_tf normalized Q(s,a) and normalized critic_target y
# ( not use denormalized version ) as loss function, for two different critic, we need to train them both
self.critic_loss = 0
# merge the critic loss for all the Q value functions
for normalized_critic_tf, critic_target_tf in zip(self.normalized_critic_tfs,self.target_Q_vals):
normalized_critic_target_tf = tf.clip_by_value(normalize(tf.stop_gradient(critic_target_tf), self.ret_rms),
self.return_range[0], self.return_range[1])
self.critic_loss += tf.reduce_mean(tf.square(normalized_critic_tf - normalized_critic_target_tf))
# apply l2_regularization on some trainable variables and add them into loss function
if self.critic_l2_reg > 0.:
critic_reg_vars = [var for var in self.critic_trainable_vars if 'kernel' in var.name and 'output' not in var.name]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic_trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic_trainable_vars, clip_norm=self.clip_norm)
# un-flatten the gradients for several critics, and compute moment
self.critic_grad_array = tf.reshape(self.critic_grads,shape=[-1,len(self.critics)])
self.critic_grad_mean = tf.reduce_mean(self.critic_grad_array,axis=1)
self.critic_grad_var = reduce_var(self.critic_grad_array,axis=1)
# sum up the gradients
self.critic_grad_var_std = tf.sqrt(tf.reduce_sum(self.critic_grad_var))
# print the shape of gradients
print("[Tiancheng Shape] Critic Grad Array", self.critic_grad_array.shape)
print("[Tiancheng Shape] Critic Grad Mean", self.critic_grad_mean.shape)
print("[Tiancheng Shape] Critic Grad Variance", self.critic_grad_var.shape)
print("[Tiancheng Shape] Critic Grad VarStd", self.critic_grad_var_std.shape)
# add support to single-thread adam
if self.use_mpi_adam:
self.critic_optimizer = MpiAdam(var_list=self.critic_trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
else:
self.critic_grads = list(
zip(tf.gradients(self.critic_loss, self.critic_trainable_vars), self.critic_trainable_vars))
self.critic_optimizer = tf.train.AdamOptimizer(learning_rate=self.critic_lr, beta1=0.9, beta2=0.999,
epsilon=1e-08)
self.critic_train = self.critic_optimizer.apply_gradients(self.critic_grads)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
self.critic_output_vars = []
self.target_output_vars = []
for critic, target_critic in zip(self.critics,self.target_critics):
self.critic_output_vars += critic.output_vars
self.target_output_vars += target_critic.output_vars
for vs in [self.critic_output_vars,self.target_output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
# TODO: compute the variance of values and gradient, for both J and Q
ops += [tf.reduce_mean(self.critic_tf_mean)]
names += ['MeanQ_mean_over_states']
ops += [reduce_std(self.critic_tf_mean)]
names += ['MeanQ_std_over_states']
# print the shape of gradients
ops += [self.actor_grad_var_std]
names += ['Actor Grad Variance Std']
ops += [tf.norm(self.actor_grad_mean)]
names += ['Actor Grad Mean Norm']
# print the shape of gradients
ops += [self.critic_grad_var_std]
names += ['Critic Grad Variance Std']
ops += [tf.norm(self.critic_grad_mean)]
names += ['Critic Grad Mean Norm']
# TODO: outdated stats need to be re-arranged
# ops += [tf.reduce_mean(self.critic_tf0)]
# names += ['reference_Q0_mean']
# ops += [reduce_std(self.critic_tf0)]
# names += ['reference_Q0_std']
#
# ops += [tf.reduce_mean(self.critic_tf1)]
# names += ['reference_Q1_mean']
# ops += [reduce_std(self.critic_tf1)]
# names += ['reference_Q1_std']
#
# ops += [tf.reduce_mean(self.critic_with_actor_tf0)]
# names += ['reference_actor_Q0_mean']
# ops += [reduce_std(self.critic_with_actor_tf0)]
# names += ['reference_actor_Q0_std']
#
# ops += [tf.reduce_mean(self.critic_with_actor_tf1)]
# names += ['reference_actor_Q1_mean']
# ops += [reduce_std(self.critic_with_actor_tf1)]
# names += ['reference_actor_Q1_std']
#
# ops += [tf.reduce_mean(self.actor_tf)]
# names += ['reference_action_mean']
# ops += [reduce_std(self.actor_tf)]
# names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
# compute the action from the observation pi(s)
# has an option to compute the q function at the same time
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
# TODO: not sure what to do for this critic_with_actor_tf, set to critic_with_actor_tf0
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf_main], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self, take_update=True,stop_critic_training=False,stop_actor_training=False):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
# if self.normalize_returns and self.enable_popart:
# # compute old mean, old std and target Q values
# # old mean and std is used for normalization
# # and target Q values for
# old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
#
# # compute something
# self.ret_rms.update(target_Q.flatten())
# self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
# self.old_std : np.array([old_std]),
# self.old_mean : np.array([old_mean]),
# })
#
# # Run sanity check. Disabled by default since it slows down things considerably.
# # print('running sanity check')
# # target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# # self.obs1: batch['obs1'],
# # self.rewards: batch['rewards'],
# # self.terminals1: batch['terminals1'].astype('float32'),
# # })
# # print(target_Q_new, target_Q, new_mean, new_std)
# # assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
# else:
# # compute target Q value functions ( ( 1 - terminal ) * gamma * Q(s,pi(s)) + r )
# target_Q = self.sess.run([self.target_Q], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# Get all gradients and perform a "synced update".
# compute the gradients of actor and critic
if self.use_mpi_adam:
ops = [self.critic_grads, self.critic_loss]
critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
if not stop_critic_training:
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
if take_update:
ops = [self.actor_grads, self.actor_loss]
actor_grads, actor_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
})
if not stop_actor_training:
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
return critic_loss, actor_loss
else:
if stop_critic_training:
ops = [self.critic_grads, self.critic_grads, self.critic_loss]
else:
ops = [self.critic_train, self.critic_grads, self.critic_loss]
_, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
if take_update:
if stop_actor_training:
ops = [self.actor_grads, self.actor_grads, self.actor_loss]
else:
ops = [self.actor_train, self.actor_grads, self.actor_loss]
_, actor_grads, actor_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
})
return critic_loss, actor_loss
return critic_loss, 0
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
if self.use_mpi_adam:
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
self.obs1: self.stats_sample['obs1'],
self.rewards: self.stats_sample['rewards'],
self.terminals1: self.stats_sample['terminals1'].astype('float32'),
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0: batch['obs0'],
self.param_noise_stddev: self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
class DDPG(tf.Module):
def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.observation_shape = observation_shape
self.critic = critic
self.actor = actor
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.actor_lr = tf.constant(actor_lr)
self.critic_lr = tf.constant(critic_lr)
# Observation normalization.
if self.normalize_observations:
with tf.name_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
# Return normalization.
if self.normalize_returns:
with tf.name_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
self.target_critic = Critic(actor.nb_actions, observation_shape, name='target_critic', network=critic.network, **critic.network_kwargs)
self.target_actor = Actor(actor.nb_actions, observation_shape, name='target_actor', network=actor.network, **actor.network_kwargs)
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise()
if MPI is not None:
comm = MPI.COMM_WORLD
self.actor_optimizer = MpiAdamOptimizer(comm, self.actor.trainable_variables)
self.critic_optimizer = MpiAdamOptimizer(comm, self.critic.trainable_variables)
else:
self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=actor_lr)
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=critic_lr)
logger.info('setting up actor optimizer')
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_variables]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
logger.info('setting up critic optimizer')
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_variables]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
if self.critic_l2_reg > 0.:
critic_reg_vars = []
for layer in self.critic.network_builder.layers[1:]:
critic_reg_vars.append(layer.kernel)
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
logger.info('setting up critic target updates ...')
for var, target_var in zip(self.critic.variables, self.target_critic.variables):
logger.info(' {} <- {}'.format(target_var.name, var.name))
logger.info('setting up actor target updates ...')
for var, target_var in zip(self.actor.variables, self.target_actor.variables):
logger.info(' {} <- {}'.format(target_var.name, var.name))
if self.param_noise:
logger.info('setting up param noise')
for var, perturbed_var in zip(self.actor.variables, self.perturbed_actor.variables):
if var in actor.perturbable_vars:
logger.info(' {} <- {} + noise'.format(perturbed_var.name, var.name))
else:
logger.info(' {} <- {}'.format(perturbed_var.name, var.name))
for var, perturbed_var in zip(self.actor.variables, self.perturbed_adaptive_actor.variables):
if var in actor.perturbable_vars:
logger.info(' {} <- {} + noise'.format(perturbed_var.name, var.name))
else:
logger.info(' {} <- {}'.format(perturbed_var.name, var.name))
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.initial_state = None # recurrent architectures not supported yet
def setup_param_noise(self):
assert self.param_noise is not None
# Configure perturbed actor.
self.perturbed_actor = Actor(self.actor.nb_actions, self.observation_shape, name='param_noise_actor', network=self.actor.network, **self.actor.network_kwargs)
# Configure separate copy for stddev adoption.
self.perturbed_adaptive_actor = Actor(self.actor.nb_actions, self.observation_shape, name='adaptive_param_noise_actor', network=self.actor.network, **self.actor.network_kwargs)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
@tf.function
def step(self, obs, apply_noise=True, compute_Q=True):
normalized_obs = tf.clip_by_value(normalize(obs, self.obs_rms), self.observation_range[0], self.observation_range[1])
actor_tf = self.actor(normalized_obs)
if self.param_noise is not None and apply_noise:
action = self.perturbed_actor(normalized_obs)
else:
action = actor_tf
if compute_Q:
normalized_critic_with_actor_tf = self.critic(normalized_obs, actor_tf)
q = denormalize(tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
else:
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
action += noise
action = tf.clip_by_value(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
B = obs0.shape[0]
for b in range(B):
self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b])
if self.normalize_observations:
self.obs_rms.update(np.array([obs0[b]]))
def train(self):
batch = self.memory.sample(batch_size=self.batch_size)
obs0, obs1 = tf.constant(batch['obs0']), tf.constant(batch['obs1'])
actions, rewards, terminals1 = tf.constant(batch['actions']), tf.constant(batch['rewards']), tf.constant(batch['terminals1'], dtype=tf.float32)
normalized_obs0, target_Q = self.compute_normalized_obs0_and_target_Q(obs0, obs1, rewards, terminals1)
if self.normalize_returns and self.enable_popart:
old_mean = self.ret_rms.mean
old_std = self.ret_rms.std
self.ret_rms.update(target_Q.flatten())
# renormalize Q outputs
new_mean = self.ret_rms.mean
new_std = self.ret_rms.std
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
kernel, bias = vs
kernel.assign(kernel * old_std / new_std)
bias.assign((bias * old_std + old_mean - new_mean) / new_std)
actor_grads, actor_loss = self.get_actor_grads(normalized_obs0)
critic_grads, critic_loss = self.get_critic_grads(normalized_obs0, actions, target_Q)
if MPI is not None:
self.actor_optimizer.apply_gradients(actor_grads, self.actor_lr)
self.critic_optimizer.apply_gradients(critic_grads, self.critic_lr)
else:
self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
self.critic_optimizer.apply_gradients(zip(critic_grads, self.critic.trainable_variables))
return critic_loss, actor_loss
@tf.function
def compute_normalized_obs0_and_target_Q(self, obs0, obs1, rewards, terminals1):
normalized_obs0 = tf.clip_by_value(normalize(obs0, self.obs_rms), self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(obs1, self.obs_rms), self.observation_range[0], self.observation_range[1])
Q_obs1 = denormalize(self.target_critic(normalized_obs1, self.target_actor(normalized_obs1)), self.ret_rms)
target_Q = rewards + (1. - terminals1) * self.gamma * Q_obs1
return normalized_obs0, target_Q
@tf.function
def get_actor_grads(self, normalized_obs0):
with tf.GradientTape() as tape:
actor_tf = self.actor(normalized_obs0)
normalized_critic_with_actor_tf = self.critic(normalized_obs0, actor_tf)
critic_with_actor_tf = denormalize(tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
actor_loss = -tf.reduce_mean(critic_with_actor_tf)
actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
if self.clip_norm:
actor_grads = [tf.clip_by_norm(grad, clip_norm=self.clip_norm) for grad in actor_grads]
if MPI is not None:
actor_grads = tf.concat([tf.reshape(g, (-1,)) for g in actor_grads], axis=0)
return actor_grads, actor_loss
@tf.function
def get_critic_grads(self, normalized_obs0, actions, target_Q):
with tf.GradientTape() as tape:
normalized_critic_tf = self.critic(normalized_obs0, actions)
normalized_critic_target_tf = tf.clip_by_value(normalize(target_Q, self.ret_rms), self.return_range[0], self.return_range[1])
critic_loss = tf.reduce_mean(tf.square(normalized_critic_tf - normalized_critic_target_tf))
# The first is input layer, which is ignored here.
if self.critic_l2_reg > 0.:
# Ignore the first input layer.
for layer in self.critic.network_builder.layers[1:]:
# The original l2_regularizer takes half of sum square.
critic_loss += (self.critic_l2_reg / 2.)* tf.reduce_sum(tf.square(layer.kernel))
critic_grads = tape.gradient(critic_loss, self.critic.trainable_variables)
if self.clip_norm:
critic_grads = [tf.clip_by_norm(grad, clip_norm=self.clip_norm) for grad in critic_grads]
if MPI is not None:
critic_grads = tf.concat([tf.reshape(g, (-1,)) for g in critic_grads], axis=0)
return critic_grads, critic_loss
def initialize(self):
if MPI is not None:
sync_from_root(self.actor.trainable_variables + self.critic.trainable_variables)
self.target_actor.set_weights(self.actor.get_weights())
self.target_critic.set_weights(self.critic.get_weights())
@tf.function
def update_target_net(self):
for var, target_var in zip(self.actor.variables, self.target_actor.variables):
target_var.assign((1. - self.tau) * target_var + self.tau * var)
for var, target_var in zip(self.critic.variables, self.target_critic.variables):
target_var.assign((1. - self.tau) * target_var + self.tau * var)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
obs0 = self.stats_sample['obs0']
actions = self.stats_sample['actions']
normalized_obs0 = tf.clip_by_value(normalize(obs0, self.obs_rms), self.observation_range[0], self.observation_range[1])
normalized_critic_tf = self.critic(normalized_obs0, actions)
critic_tf = denormalize(tf.clip_by_value(normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
actor_tf = self.actor(normalized_obs0)
normalized_critic_with_actor_tf = self.critic(normalized_obs0, actor_tf)
critic_with_actor_tf = denormalize(tf.clip_by_value(normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
stats = {}
if self.normalize_returns:
stats['ret_rms_mean'] = self.ret_rms.mean
stats['ret_rms_std'] = self.ret_rms.std
if self.normalize_observations:
stats['obs_rms_mean'] = tf.reduce_mean(self.obs_rms.mean)
stats['obs_rms_std'] = tf.reduce_mean(self.obs_rms.std)
stats['reference_Q_mean'] = tf.reduce_mean(critic_tf)
stats['reference_Q_std'] = reduce_std(critic_tf)
stats['reference_actor_Q_mean'] = tf.reduce_mean(critic_with_actor_tf)
stats['reference_actor_Q_std'] = reduce_std(critic_with_actor_tf)
stats['reference_action_mean'] = tf.reduce_mean(actor_tf)
stats['reference_action_std'] = reduce_std(actor_tf)
if self.param_noise:
perturbed_actor_tf = self.perturbed_actor(normalized_obs0)
stats['reference_perturbed_action_mean'] = tf.reduce_mean(perturbed_actor_tf)
stats['reference_perturbed_action_std'] = reduce_std(perturbed_actor_tf)
stats.update(self.param_noise.get_stats())
return stats
def adapt_param_noise(self, obs0):
try:
from mpi4py import MPI
except ImportError:
MPI = None
if self.param_noise is None:
return 0.
mean_distance = self.get_mean_distance(obs0).numpy()
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(mean_distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
@tf.function
def get_mean_distance(self, obs0):
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
update_perturbed_actor(self.actor, self.perturbed_adaptive_actor, self.param_noise.current_stddev)
normalized_obs0 = tf.clip_by_value(normalize(obs0, self.obs_rms), self.observation_range[0], self.observation_range[1])
actor_tf = self.actor(normalized_obs0)
adaptive_actor_tf = self.perturbed_adaptive_actor(normalized_obs0)
mean_distance = tf.sqrt(tf.reduce_mean(tf.square(actor_tf - adaptive_actor_tf)))
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
update_perturbed_actor(self.actor, self.perturbed_actor, self.param_noise.current_stddev)
class Model(object):
def __init__(self, network, env, gamma=1, tau=0.01, total_timesteps=1e6,
normalize_observations=True, normalize_returns=False, enable_popart=False,
noise_type='adaptive-param_0.2', clip_norm=None, reward_scale=1.,
batch_size=128, l2_reg_coef=0.2, actor_lr=1e-4, critic_lr=1e-3,
observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
**network_kwargs):
# logger.info('Using agent with the following configuration:')
# logger.info(str(self.__dict__.items()))
observation_shape = env.observation_space.shape
action_shape = env.action_space.shape
# Inputs.
self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
# Parameters.
self.env = env
self.gamma = gamma
self.tau = tau
self.total_timesteps = total_timesteps
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.enable_popart = enable_popart
self.clip_norm = clip_norm
self.reward_scale = reward_scale
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.batch_size = batch_size
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.l2_reg_coef = l2_reg_coef
self.stats_sample = None
self.action_noise = None
self.param_noise = None
nb_actions = self.env.action_space.shape[-1]
if noise_type is not None:
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
self.param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev),
desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
self.action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
self.action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
self.memory = Memory(limit=int(1e6), action_shape=env.action_space.shape,
observation_shape=env.observation_space.shape)
self.critic = Critic(network=network, **network_kwargs)
self.actor = Actor(nb_actions, network=network, **network_kwargs)
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(self.actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(self.critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = self.actor(normalized_obs0)
self.normalized_critic_tf = self.critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = self.critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.initial_state = None # recurrent architectures not supported yet
self.def_path_pre = os.path.dirname(os.path.abspath(__file__)) + '/tmp/'
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars,
self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor,
self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms),
self.return_range[0], self.return_range[1])
self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.l2_reg_coef > 0.:
critic_reg_vars = [var for var in self.critic.trainable_vars if
var.name.endswith('/w:0') and 'output' not in var.name]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.l2_reg_coef))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.l2_reg_coef),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def train_step(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action[0].shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def step(self, obs, compute_Q=True):
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
if compute_Q:
action, q = self.sess.run([self.actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(self.actor_tf, feed_dict=feed_dict)
q = None
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
B = obs0.shape[0]
for b in range(B):
self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b])
if self.normalize_observations:
self.obs_rms.update(np.array([obs0[b]]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q, feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
try:
from mpi4py import MPI
except ImportError:
MPI = None
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0: batch['obs0'],
self.param_noise_stddev: self.param_noise.current_stddev,
})
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
def learn(self,
total_timesteps=None,
seed=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=20,
nb_rollout_steps=100,
render=False,
nb_train_steps=50, # per epoch cycle and MPI worker,
batch_size=64, # per MPI worker
param_noise_adaption_interval=50,):
set_global_seeds(seed)
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // (nb_epoch_cycles * nb_rollout_steps)
else:
nb_epochs = 500
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
else:
rank = 0
# eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
sess = U.get_session()
# Prepare everything.
self.initialize(sess)
sess.graph.finalize()
self.reset()
obs = self.env.reset()
# if eval_env is not None:
# eval_obs = eval_env.reset()
nenvs = obs.shape[0]
episode_reward = np.zeros(nenvs, dtype=np.float32) # vector
episode_step = np.zeros(nenvs, dtype=int) # vector
episodes = 0 # scalar
t = 0 # scalar
start_time = time.time()
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
for epoch in range(nb_epochs):
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
if nenvs > 1:
# if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
# of the environments, so resetting here instead
self.reset()
for t_rollout in range(nb_rollout_steps):
# Predict next action.
action, q, _, _ = self.train_step(obs, apply_noise=True, compute_Q=True)
# Execute next action.
if rank == 0 and render:
self.env.render()
# max_action is of dimension A, whereas action is dimension (nenvs, A) - the multiplication gets broadcasted to the batch
# new_obs, r, done, info = self.env.step(max_action * action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
new_obs, r, done, info = self.env.step(action)
# note these outputs are batched from vecenv
t += 1
if rank == 0 and render:
self.env.render()
episode_reward += r
episode_step += 1
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
# the batched data will be unrolled in memory.py's append.
self.store_transition(obs, action, r, new_obs, done)
obs = new_obs
for d in range(len(done)):
if done[d]:
# Episode done.
epoch_episode_rewards.append(episode_reward[d])
episode_rewards_history.append(episode_reward[d])
epoch_episode_steps.append(episode_step[d])
episode_reward[d] = 0.
episode_step[d] = 0
epoch_episodes += 1
episodes += 1
if nenvs == 1:
self.reset()
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
for t_train in range(nb_train_steps):
# Adapt param noise, if necessary.
if self.memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
distance = self.adapt_param_noise()
epoch_adaptive_distances.append(distance)
cl, al = self.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
self.update_target_net()
#
# # Evaluate.
# eval_episode_rewards = []
# eval_qs = []
# if eval_env is not None:
# eval_obs = eval_env.reset()
# nenvs_eval = eval_obs.shape[0]
# eval_episode_reward = np.zeros(nenvs_eval, dtype=np.float32)
# for t_rollout in range(nb_eval_steps):
# eval_action, eval_q, _, _ = self.train_step(eval_obs, apply_noise=False, compute_Q=True)
# # eval_obs, eval_r, eval_done, eval_info = eval_env.step(
# # max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
# eval_obs, eval_r, eval_done, eval_info = eval_env.step(eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
#
# if render_eval:
# eval_env.render()
# eval_episode_reward += eval_r
#
# eval_qs.append(eval_q)
# for d in range(len(eval_done)):
# if eval_done[d]:
# eval_episode_rewards.append(eval_episode_reward[d])
# eval_episode_rewards_history.append(eval_episode_reward[d])
# eval_episode_reward[d] = 0.0
if MPI is not None:
mpi_size = MPI.COMM_WORLD.Get_size()
else:
mpi_size = 1
# save trainable variables
file_name = time.strftime('Y%YM%mD%d_h%Hm%Ms%S', time.localtime(time.time()))
model_save_path = self.def_path_pre + file_name
self.save(model_save_path)
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = self.get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_std'] = np.std(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
combined_stats['rollout/return_history_std'] = np.std(episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
# combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
# combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
# combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
# Evaluation statistics.
# if eval_env is not None:
# combined_stats['eval/return'] = eval_episode_rewards
# combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)
# combined_stats['eval/Q'] = eval_qs
# combined_stats['eval/episodes'] = len(eval_episode_rewards)
combined_stats_sums = np.array([np.array(x).flatten()[0] for x in combined_stats.values()])
if MPI is not None:
combined_stats_sums = MPI.COMM_WORLD.allreduce(combined_stats_sums)
combined_stats = {k: v / mpi_size for (k, v) in zip(combined_stats.keys(), combined_stats_sums)}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(self.env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(self.env.get_state(), f)
# if eval_env and hasattr(eval_env, 'get_state'):
# with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
# pickle.dump(eval_env.get_state(), f)
self.sess.graph._unsafe_unfinalize()
return self
def save(self, save_path=None):
save_variables(save_path=save_path, sess=self.sess)
print('save model variables to', save_path)
def load_newest(self, load_path=None):
file_list = os.listdir(self.def_path_pre)
file_list.sort(key=lambda x: os.path.getmtime(os.path.join(self.def_path_pre, x)))
if load_path is None:
load_path = os.path.join(self.def_path_pre, file_list[-1])
load_variables(load_path=load_path, sess=self.sess)
print('load_path: ', load_path)
def load_index(self, index, load_path=None):
file_list = os.listdir(self.def_path_pre)
file_list.sort(key=lambda x: os.path.getmtime(os.path.join(self.def_path_pre, x)), reverse=True)
if load_path is None:
load_path = os.path.join(self.def_path_pre, file_list[index])
load_variables(load_path=load_path, sess=self.sess)
print('load_path: ', load_path)
class DDPG(object):
def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.,
td3_variant=False,td3_policy_freq=1,td3_policy_noise=0.0,td3_noise_clip=0.5):
# Inputs.
self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
#추가된 내용
#parameters for using TD3 variant of DDPG
#https://arxiv.org/abs/1802.09477
self.td3_variant = td3_variant
self.td3_policy_freq = td3_policy_freq
self.td3_policy_noise = td3_policy_noise
self.td3_noise_clip = td3_noise_clip
#노말라이제이션 코드 her에서 가져오자.
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
if self.td3_variant:
logger.info('using TD3 variant model')
self.normalized_critic_tf, self.normalized_critic_tf2 = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf, _ = critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]),
self.ret_rms)
out_q1, out_q2 = target_critic(normalized_obs1, target_actor(normalized_obs1))
min_q1 = tf.minimum(out_q1,out_q2)
Q_obs1 = denormalize(min_q1, self.ret_rms)
else:
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.initial_state = None # recurrent architectures not supported yet
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.actor_target_soft_updates = actor_soft_updates
self.critic_target_soft_updates = critic_soft_updates
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms),
self.return_range[0], self.return_range[1])
if self.td3_variant:
logger.info('using TD3 variant loss')
self.critic_loss = tf.losses.mean_squared_error(normalized_critic_target_tf,self.normalized_critic_tf) \
+ tf.losses.mean_squared_error(normalized_critic_target_tf,self.normalized_critic_tf2)
else:
self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def step(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action[0].shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
B = obs0.shape[0]
for b in range(B):
self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b])
if self.normalize_observations:
self.obs_rms.update(np.array([obs0[b]]))
def train(self,train_iter):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
self.old_std : np.array([old_std]),
self.old_mean : np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q, feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
if self.td3_policy_noise > 0:
noise = np.random.normal(loc=0.0,scale=self.td3_policy_noise,size=np.shape(batch['actions']))
noise = np.clip(noise,-self.td3_noise_clip,self.td3_noise_clip)
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: np.clip(batch['actions'] + noise,self.action_range[0],self.action_range[1]),
self.critic_target: target_Q,
})
else:
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
#TD3 has hyperparameter for how frequently to update actor policy and target networks
if train_iter % self.td3_policy_freq == 0:
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self, train_iter):
# TD3 has hyperparameter for how frequently to update actor policy and target networks
if train_iter % self.td3_policy_freq == 0:
self.sess.run(self.actor_target_soft_updates)
self.sess.run(self.critic_target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0: batch['obs0'],
self.param_noise_stddev: self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
class SAC(RLAlgorithm):
"""Soft Actor-Critic (SAC)
References
----------
[1] Tuomas Haarnoja*, Aurick Zhou*, Kristian Hartikainen*, George Tucker,
Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter
Abbeel, and Sergey Levine. Soft Actor-Critic Algorithms and
Applications. arXiv preprint arXiv:1812.05905. 2018.
"""
def __init__(
self,
training_environment,
evaluation_environment,
policy,
Qs,
pool,
plotter=None,
lr=3e-4,
reward_scale=1.0,
target_entropy='auto',
discount=0.99,
tau=5e-3,
target_update_interval=1,
action_prior='uniform',
reparameterize=False,
store_extra_policy_info=False,
save_full_state=False,
**kwargs,
):
"""
Args:
env (`SoftlearningEnv`): Environment used for training.
policy: A policy function approximator.
initial_exploration_policy: ('Policy'): A policy that we use
for initial exploration which is not trained by the algorithm.
Qs: Q-function approximators. The min of these
approximators will be used. Usage of at least two Q-functions
improves performance by reducing overestimation bias.
pool (`PoolBase`): Replay pool to add gathered samples to.
plotter (`QFPolicyPlotter`): Plotter instance to be used for
visualizing Q-function during training.
lr (`float`): Learning rate used for the function approximators.
discount (`float`): Discount factor for Q-function updates.
tau (`float`): Soft value function target update weight.
target_update_interval ('int'): Frequency at which target network
updates occur in iterations.
reparameterize ('bool'): If True, we use a gradient estimator for
the policy derived using the reparameterization trick. We use
a likelihood ratio based estimator otherwise.
"""
super(SAC, self).__init__(**kwargs)
self._training_environment = training_environment
self._evaluation_environment = evaluation_environment
self._policy = policy
self._Qs = Qs
self._Q_targets = tuple(tf.keras.models.clone_model(Q) for Q in Qs)
self._pool = pool
self._plotter = plotter
self._policy_lr = lr
self._Q_lr = lr
self.value_rms = RunningMeanStd(shape=(1,))
self._reward_scale = reward_scale
self._target_entropy = (
-np.prod(self._training_environment.action_space.shape)
if target_entropy == 'auto'
else target_entropy)
self._discount = discount
self._tau = tau
self._target_update_interval = target_update_interval
self._action_prior = action_prior
self._reparameterize = reparameterize
self._store_extra_policy_info = store_extra_policy_info
self._save_full_state = save_full_state
observation_shape = self._training_environment.active_observation_shape
action_shape = self._training_environment.action_space.shape
assert len(observation_shape) == 1, observation_shape
self._observation_shape = observation_shape
assert len(action_shape) == 1, action_shape
self._action_shape = action_shape
self._build()
def _build(self):
self._training_ops = {}
self._init_global_step()
self._init_placeholders()
self._init_actor_update()
self._init_critic_update()
self._init_diagnostics_ops()
def _init_placeholders(self):
"""Create input placeholders for the SAC algorithm.
Creates `tf.placeholder`s for:
- observation
- next observation
- action
- reward
- terminals
"""
self._iteration_ph = tf.placeholder(
tf.int64, shape=None, name='iteration')
self._observations_ph = tf.placeholder(
tf.float32,
shape=(None, *self._observation_shape),
name='observation',
)
self._next_observations_ph = tf.placeholder(
tf.float32,
shape=(None, *self._observation_shape),
name='next_observation',
)
self._actions_ph = tf.placeholder(
tf.float32,
shape=(None, *self._action_shape),
name='actions',
)
self._rewards_ph = tf.placeholder(
tf.float32,
shape=(None, 1),
name='rewards',
)
self._terminals_ph = tf.placeholder(
tf.float32,
shape=(None, 1),
name='terminals',
)
if self._store_extra_policy_info:
self._log_pis_ph = tf.placeholder(
tf.float32,
shape=(None, 1),
name='log_pis',
)
self._raw_actions_ph = tf.placeholder(
tf.float32,
shape=(None, *self._action_shape),
name='raw_actions',
)
def _get_Q_target(self):
next_actions = self._policy.actions([self._next_observations_ph])
next_log_pis = self._policy.log_pis(
[self._next_observations_ph], next_actions)
next_Qs_values = tuple(
Q([self._next_observations_ph, next_actions]) * self.value_rms.std + self.value_rms.mean
for Q in self._Q_targets)
min_next_Q = tf.reduce_min(next_Qs_values, axis=0)
next_value = min_next_Q - self._alpha * next_log_pis
Q_target = td_target(
reward=self._reward_scale * self._rewards_ph,
discount=self._discount,
next_value=(1 - self._terminals_ph) * next_value)
return (Q_target - self.value_rms.mean)/self.value_rms.std, Q_target
def _init_critic_update(self):
"""Create minimization operation for critic Q-function.
Creates a `tf.optimizer.minimize` operation for updating
critic Q-function with gradient descent, and appends it to
`self._training_ops` attribute.
See Equations (5, 6) in [1], for further information of the
Q-function update rule.
"""
Q_target, self.raw_q_target = [q[0] for q in tf.split(tf.stop_gradient(self._get_Q_target()), axis=0, num_or_size_splits=2)]
assert Q_target.shape.as_list() == [None, 1]
Q_values = self._Q_values = tuple(
Q([self._observations_ph, self._actions_ph])
for Q in self._Qs)
Q_losses = self._Q_losses = tuple(
tf.losses.mean_squared_error(
labels=Q_target, predictions=Q_value, weights=0.5)
for Q_value in Q_values)
self._Q_optimizers = tuple(
tf.train.AdamOptimizer(
learning_rate=self._Q_lr,
name='{}_{}_optimizer'.format(Q._name, i)
) for i, Q in enumerate(self._Qs))
Q_training_ops = tuple(
Q_optimizer.minimize(loss=Q_loss, var_list=Q.trainable_variables)
for i, (Q, Q_loss, Q_optimizer)
in enumerate(zip(self._Qs, Q_losses, self._Q_optimizers)))
self._training_ops.update({'Q': tf.group(Q_training_ops)})
def _init_actor_update(self):
"""Create minimization operations for policy and entropy.
Creates a `tf.optimizer.minimize` operations for updating
policy and entropy with gradient descent, and adds them to
`self._training_ops` attribute.
See Section 4.2 in [1], for further information of the policy update,
and Section 5 in [1] for further information of the entropy update.
"""
actions = self._policy.actions([self._observations_ph])
log_pis = self._policy.log_pis([self._observations_ph], actions)
assert log_pis.shape.as_list() == [None, 1]
log_alpha = self._log_alpha = tf.get_variable(
'log_alpha',
dtype=tf.float32,
initializer=0.0)
alpha = tf.exp(log_alpha)
if isinstance(self._target_entropy, Number):
alpha_loss = -tf.reduce_mean(
log_alpha * tf.stop_gradient(log_pis + self._target_entropy))
self._alpha_optimizer = tf.train.AdamOptimizer(
self._policy_lr, name='alpha_optimizer')
self._alpha_train_op = self._alpha_optimizer.minimize(
loss=alpha_loss, var_list=[log_alpha])
self._training_ops.update({
'temperature_alpha': self._alpha_train_op
})
self._alpha = alpha
if self._action_prior == 'normal':
policy_prior = tfp.distributions.MultivariateNormalDiag(
loc=tf.zeros(self._action_shape),
scale_diag=tf.ones(self._action_shape))
policy_prior_log_probs = policy_prior.log_prob(actions)
elif self._action_prior == 'uniform':
policy_prior_log_probs = 0.0
Q_log_targets = tuple(
Q([self._observations_ph, actions])
for Q in self._Qs)
min_Q_log_target = tf.reduce_min(Q_log_targets, axis=0)
if self._reparameterize:
policy_kl_losses = (
alpha * log_pis
- min_Q_log_target
- policy_prior_log_probs)
else:
raise NotImplementedError
assert policy_kl_losses.shape.as_list() == [None, 1]
self._policy_losses = policy_kl_losses
policy_loss = tf.reduce_mean(policy_kl_losses)
self._policy_optimizer = tf.train.AdamOptimizer(
learning_rate=self._policy_lr,
name="policy_optimizer")
policy_train_op = self._policy_optimizer.minimize(
loss=policy_loss,
var_list=self._policy.trainable_variables)
self._training_ops.update({'policy_train_op': policy_train_op})
def _init_diagnostics_ops(self):
diagnosables = OrderedDict((
('Q_value', self._Q_values),
('Q_loss', self._Q_losses),
('policy_loss', self._policy_losses),
('alpha', self._alpha)
))
diagnostic_metrics = OrderedDict((
('mean', tf.reduce_mean),
('std', lambda x: tfp.stats.stddev(x, sample_axis=None)),
))
self._diagnostics_ops = OrderedDict([
(f'{key}-{metric_name}', metric_fn(values))
for key, values in diagnosables.items()
for metric_name, metric_fn in diagnostic_metrics.items()
])
def _init_training(self):
self._update_target(tau=1.0)
def _update_target(self, tau=None):
tau = tau or self._tau
for Q, Q_target in zip(self._Qs, self._Q_targets):
source_params = Q.get_weights()
target_params = Q_target.get_weights()
Q_target.set_weights([
tau * source + (1.0 - tau) * target
for source, target in zip(source_params, target_params)
])
def _do_training(self, iteration, batch):
"""Runs the operations for updating training and target ops."""
feed_dict = self._get_feed_dict(iteration, batch)
val = self._session.run([self.raw_q_target, self._training_ops], feed_dict)
self.value_rms.update(val[0])
if iteration % self._target_update_interval == 0:
# Run target ops here.
self._update_target()
def _get_feed_dict(self, iteration, batch):
"""Construct TensorFlow feed_dict from sample batch."""
feed_dict = {
self._observations_ph: batch['observations'],
self._actions_ph: batch['actions'],
self._next_observations_ph: batch['next_observations'],
self._rewards_ph: batch['rewards'],
self._terminals_ph: batch['terminals'],
}
if self._store_extra_policy_info:
feed_dict[self._log_pis_ph] = batch['log_pis']
feed_dict[self._raw_actions_ph] = batch['raw_actions']
if iteration is not None:
feed_dict[self._iteration_ph] = iteration
return feed_dict
def get_diagnostics(self,
iteration,
batch,
training_paths,
evaluation_paths):
"""Return diagnostic information as ordered dictionary.
Records mean and standard deviation of Q-function and state
value function, and TD-loss (mean squared Bellman error)
for the sample batch.
Also calls the `draw` method of the plotter, if plotter defined.
"""
feed_dict = self._get_feed_dict(iteration, batch)
diagnostics = self._session.run(self._diagnostics_ops, feed_dict)
diagnostics.update(OrderedDict([
(f'policy/{key}', value)
for key, value in
self._policy.get_diagnostics(batch['observations']).items()
]))
if self._plotter:
self._plotter.draw()
return diagnostics
@property
def tf_saveables(self):
saveables = {
'_policy_optimizer': self._policy_optimizer,
**{
f'Q_optimizer_{i}': optimizer
for i, optimizer in enumerate(self._Q_optimizers)
},
'_log_alpha': self._log_alpha,
}
if hasattr(self, '_alpha_optimizer'):
saveables['_alpha_optimizer'] = self._alpha_optimizer
return saveables
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
adaptive_param_noise=True,
adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.,
aux_apply='both',
aux_tasks=[],
aux_lambdas={}):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
self.norm_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
self.norm_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Aux Inputs.
self.aux_apply = aux_apply
self.aux_tasks = aux_tasks
self.aux_lambdas = aux_lambdas
if 'prop' in self.aux_tasks or 'caus' in self.aux_tasks or 'repeat' in self.aux_tasks:
self.obs100 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs100')
self.obs101 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs101')
self.actions100 = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions100')
self.norm_obs100 = tf.clip_by_value(
normalize(self.obs100, self.obs_rms),
self.observation_range[0], self.observation_range[1])
self.norm_obs101 = tf.clip_by_value(
normalize(self.obs101, self.obs_rms),
self.observation_range[0], self.observation_range[1])
if 'caus' in self.aux_tasks:
self.rewards100 = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards100')
# Create target networks.
target_actor = deepcopy(actor)
target_actor.name = 'target_actor'
target_actor.repr.name = 'target_actor_repr'
self.target_actor = target_actor
target_critic = deepcopy(critic)
target_critic.name = 'target_critic'
target_critic.repr.name = 'target_critic_repr'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(self.norm_obs0)
self.normalized_critic_tf = critic(self.norm_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(self.norm_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(
target_critic(self.norm_obs1, target_actor(self.norm_obs1)),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(self.norm_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
if self.aux_tasks:
logger.info("aux_tasks:{}".format(self.aux_tasks))
self.setup_aux_optimizer()
def setup_aux_optimizer(self):
logger.info('setting up aux optimizer for actor...')
# check if unknown or duplicate aux tasks have been given
for task in self.aux_tasks:
if not task in ("tc", "prop", "caus", "repeat", "predict"):
raise ValueError("!! task {} not implemented !!".format(task))
if self.aux_tasks.count(task) > 1:
raise ValueError(
"!! multiple tasks {} given, not valid !!".format(task))
self.aux_ops = []
self.aux_losses = tf.Variable(tf.zeros([], dtype=np.float32),
name="loss")
self.aux_vars = set([])
reprowners = []
if self.aux_apply is 'actor' or 'both':
reprowners.append(self.actor)
if self.aux_apply is 'critic' or 'both':
reprowners.append(self.critic)
for owner in reprowners:
if any(task in self.aux_tasks
for task in ("tc", "prop", "caus", "repeat")):
representation = Representation(name=owner.repr.name,
layer_norm=owner.layer_norm)
self.aux_vars.update(set(representation.trainable_vars))
s0 = representation(self.norm_obs0, reuse=True)
if any(task in self.aux_tasks
for task in ("tc", "prop", "repeat")):
s1 = representation(self.norm_obs1, reuse=True)
if any(task in self.aux_tasks
for task in ("prop", "caus", "repeat")):
s100 = representation(self.norm_obs100, reuse=True)
if any(task in self.aux_tasks for task in ("prop", "repeat")):
s101 = representation(self.norm_obs101, reuse=True)
if 'tc' in self.aux_tasks:
# temporal coherence loss is the sum of two terms:
# a - loss is present for small state changes brought by big actions
# b - loss is present for big state changes brought by small actions
# (similarity here is used as inversion mechanism)
tc_loss_a = similarity(magnitude(s1 - s0)) * magnitude(
self.actions)
tc_loss_b = similarity(magnitude(
self.actions)) * magnitude(s1 - s0)
self.tc_loss = tf.reduce_mean(tc_loss_a + tc_loss_b)
self.aux_losses += normalize_loss(self.tc_loss)
if 'prop' in self.aux_tasks:
# proportionality loss:
# punish the difference in magnitude of state change, given action similarity
# for two unrelated steps
dsmag0 = magnitude(s1 - s0)
dsmag100 = magnitude(s101 - s100)
dsmagdiff = tf.square(dsmag100 - dsmag0)
actmagsim = similarity(
magnitude(self.actions100 - self.actions))
self.prop_loss = tf.reduce_mean(dsmagdiff * actmagsim)
self.aux_losses += normalize_loss(self.prop_loss)
if 'caus' in self.aux_tasks:
# causality loss:
# punish similarity in state, given action similarity and reward difference
# for two unrelated steps
s_sim = similarity(magnitude(s100 - s0))
a_sim = similarity(magnitude(self.actions100 - self.actions))
r_diff = magnitude(self.rewards100 - self.rewards)
self.caus_loss = tf.reduce_mean(s_sim * a_sim * r_diff)
self.aux_losses += normalize_loss(self.caus_loss)
if 'repeat' in self.aux_tasks:
# repeatability loss:
# punish difference in state change, given state and action similarity
# for two unrelated steps
ds0 = s1 - s0
ds100 = s101 - s100
dsdiff = magnitude(ds100 - ds0)
s_sim = similarity(magnitude(s100 - s0))
a_sim = similarity(magnitude(self.actions100 - self.actions))
self.repeat_loss = tf.reduce_mean(dsdiff * s_sim * a_sim)
self.aux_losses += normalize_loss(self.repeat_loss)
if 'predict' in self.aux_tasks:
# prediction loss:
# punish the difference between the actual and predicted next step
predictor = Predictor(name=owner.name,
layer_norm=owner.layer_norm)
reconstr = predictor(self.norm_obs0, self.actions, reuse=True)
self.pred_loss = tf.nn.l2_loss(self.norm_obs1 - reconstr)
self.aux_losses += normalize_loss(self.pred_loss)
self.aux_vars.update(set(predictor.trainable_vars))
self.aux_losses = self.aux_losses / (2 * len(self.aux_tasks))
self.aux_vars = list(self.aux_vars)
self.aux_grads = U.flatgrad(self.aux_losses,
self.aux_vars,
clip_norm=self.clip_norm)
self.aux_optimizer = MpiAdam(var_list=self.aux_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
param_noise_actor.repr.name = 'param_noise_actor_repr'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_param_noise_actor.repr.name = 'adaptive_param_noise_actor_repr'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(normalize_loss(self.actor_loss),
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(normalize_loss(self.critic_loss),
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
if self.aux_tasks is not None:
batch = self.memory.sampletwice(batch_size=self.batch_size)
else:
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get gradients DDPG
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
feed_dict = {
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q
}
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops, feed_dict=feed_dict)
#print("actor grads norm: {}".format(np.linalg.norm(actor_grads)))
#print("critic grads norm: {}".format(np.linalg.norm(critic_grads)))
# Perform a synced update.
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
auxoutputs = []
# Get gradients AUX
if self.aux_tasks:
aux_dict = {}
aux_ops = {'aux_grads': self.aux_grads}
for index, auxtask in enumerate(self.aux_tasks):
if auxtask == 'tc':
aux_dict.update({
self.obs0: batch['obs0'],
self.obs1: batch['obs1'],
self.actions: batch['actions']
})
aux_ops.update({'tc': self.tc_loss})
if auxtask == 'prop':
aux_dict.update({
self.obs0: batch['obs0'],
self.obs1: batch['obs1'],
self.obs100: batch['obs100'],
self.obs101: batch['obs101'],
self.actions: batch['actions'],
self.actions100: batch['actions100']
})
aux_ops.update({'prop': self.prop_loss})
if auxtask == 'caus':
aux_dict.update({
self.obs0: batch['obs0'],
self.obs100: batch['obs100'],
self.actions: batch['actions'],
self.actions100: batch['actions100'],
self.rewards: batch['rewards'],
self.rewards100: batch['rewards100']
})
aux_ops.update({'caus': self.caus_loss})
if auxtask == 'repeat':
aux_dict.update({
self.obs0: batch['obs0'],
self.obs1: batch['obs1'],
self.obs100: batch['obs100'],
self.obs101: batch['obs101'],
self.actions: batch['actions'],
self.actions100: batch['actions100']
})
aux_ops.update({'repeat': self.repeat_loss})
if auxtask == 'predict':
aux_dict.update({
self.obs0: batch['obs0'],
self.obs1: batch['obs1'],
self.actions: batch['actions']
})
aux_ops.update({'predict': self.pred_loss})
auxoutputs = self.sess.run(aux_ops, feed_dict=aux_dict)
auxgrads = auxoutputs['aux_grads']
# add act and crit grads to auxoutputs
auxoutputs['actor_grads'] = actor_grads
auxoutputs['critic_grads'] = critic_grads
#print("aux grads norm: {}".format(np.linalg.norm(auxgrads)))
self.aux_optimizer.update(auxgrads, stepsize=self.actor_lr)
return critic_loss, actor_loss, auxoutputs
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class ActorLearner(object):
def __init__(self, name, actor, memory, observation_shape, action_shape,
gamma=0.95, tau=0.001, normalize_observations=True,
batch_size=128, observation_range=(-5., 5.), return_range=(-np.inf, np.inf),
actor_l2_reg=0., actor_lr=5e-5, clip_norm=None, ):
# Inputs.
self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='expert_actor_obs0')
self.action_target = tf.placeholder(tf.float32, shape=(None,) + action_shape, name=name+'action_target')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.return_range = return_range
self.observation_range = observation_range
self.clip_norm = clip_norm
self.batch_size = batch_size
self.stats_sample = None
self.actor_l2_reg = actor_l2_reg
self.actor = actor
self.actor_lr = actor_lr
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope(name + 'obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
# Set up parts.
self.setup_actor_optimizer()
self.setup_stats()
self.initial_state = None # recurrent architectures not supported yet
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = tf.reduce_mean(tf.square(self.actor_tf - self.action_target))
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = tf.train.AdamOptimizer(learning_rate=self.actor_lr)
self.optimize_expr = self.actor_optimizer.minimize(self.actor_loss, var_list=self.actor.trainable_vars)
def setup_stats(self):
ops = []
names = []
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
self.stats_ops = ops
self.stats_names = names
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss]
actor_grads, actor_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.action_target: batch['actions'],
})
# with self.graph.as_default():
self.optimize_expr.run(session=self.sess,
feed_dict={
self.obs0: batch['obs0'],
self.action_target: batch['actions'],
}
)
return actor_loss
def initialize(self, sess):
self.sess = sess
def save(self, path):
save_variables(path)
def load(self, path):
load_variables(path)
def store_transition(self, obs0, action):
# B = obs0.shape[0]
# for b in range(B):
self.memory.append(obs0, action)
if self.normalize_observations:
self.obs_rms.update(obs0)
print("Stored ", obs0.shape)
def __call__(self, obs):
# with self.graph.as_default():
print("Expert Actor call")
feed_dict = {self.obs0: U.adjust_shape(self.obs0, obs)}
# import IPython; IPython.embed()
action = self.sess.run([self.actor_tf], feed_dict=feed_dict)
print("Expert Actor return")
return action
class MADDPG(object):
def __init__(self,
name,
actor,
critic,
memory,
obs_space_n,
act_space_n,
agent_index,
obs_rms,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
self.name = name
self.num_agents = len(obs_space_n)
self.agent_index = agent_index
from gym import spaces
continuous_ctrl = not isinstance(act_space_n[0], spaces.Discrete)
# TODO: remove after testing
assert continuous_ctrl
# Multi-agent inputs
# self.obs0 = []
# self.obs1 = []
self.actions = []
# self.norm_obs0_ph = []
# self.norm_obs1_ph = []
self.obs0 = tf.placeholder(tf.float32,
shape=(
self.num_agents,
None,
) + obs_space_n[self.agent_index].shape,
name="obs0")
self.obs1 = tf.placeholder(tf.float32,
shape=(
self.num_agents,
None,
) + obs_space_n[self.agent_index].shape,
name="obs1")
# if continuous_ctrl:
# self.actions = tf.placeholder(tf.float32, shape=(self.num_agents, None,) + act_space_n[self.agent_index].shape, name="action")
# else:
# act_pdtype_n = [make_pdtype(act_space) for act_space in act_space_n]
# self.actions = [act_pdtype_n[i].sample_placeholder([None], name="action"+str(i)) for i in range(len(act_space_n))]
# this is required to reshape obs and actions for concatenation
obs_shape_list = [self.num_agents] + list(
obs_space_n[self.agent_index].shape)
act_shape_list = [self.num_agents] + list(
act_space_n[self.agent_index].shape)
self.obs_shape_prod = np.prod(obs_shape_list)
self.act_shape_prod = np.prod(act_shape_list)
for i in range(self.num_agents):
# each obs in obs0,obs1 contains info about ego agent and relative pos/vel of other agents
# self.obs0.append(tf.placeholder(tf.float32, shape=[None] + list(obs_space_n[i].shape), name="obs0_"+str(i)))
# self.obs1.append(tf.placeholder(tf.float32, shape=[None] + list(obs_space_n[i].shape), name="obs1_"+str(i)))
if continuous_ctrl:
self.actions.append(
tf.placeholder(tf.float32,
shape=[None] + list(act_space_n[i].shape),
name="action" + str(i)))
else:
self.actions.append(
make_pdtype(act_space_n[i]).sample_placeholder(
[None], name="action" + str(i)))
# self.norm_obs0_ph.append(tf.placeholder(tf.float32, shape=[None] + list(obs_space_n[i].shape), name="norm_obs0_"+str(i)))
# self.norm_obs1_ph.append(tf.placeholder(tf.float32, shape=[None] + list(obs_space_n[i].shape), name="norm_obs1_"+str(i)))
# self.norm_obs0_ph = tf.placeholder(tf.float32, shape=[self.num_agents, None] + list(obs_space_n[self.agent_index].shape), name="norm_obs0")
# self.norm_obs1_ph = tf.placeholder(tf.float32, shape=[self.num_agents, None] + list(obs_space_n[self.agent_index].shape), name="norm_obs1")
# we only provide single agent inputs for these placeholders
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
# TODO: need to update the replay buffer storage function to account for multiple agents
if self.normalize_observations:
self.obs_rms = obs_rms
else:
self.obs_rms = None
# Need to transpose observations so we can normalize them
# converts tensor to shape (batch_size, num_agents, space_size)
# transose on dim 0 and 1, leave dim 2 unchanged
obs0_t = tf.transpose(self.obs0, perm=[1, 0, 2])
obs1_t = tf.transpose(self.obs1, perm=[1, 0, 2])
actions_t = tf.transpose(self.actions, perm=[1, 0, 2])
# each entry in obs_t is normalized wrt the agent
normalized_obs0 = tf.clip_by_value(normalize(obs0_t, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(obs1_t, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# convert the obs to original shape after normalization for convenience
normalized_act_obs0 = tf.transpose(normalized_obs0, perm=[1, 0, 2])
normalized_act_obs1 = tf.transpose(normalized_obs1, perm=[1, 0, 2])
# need to specify exact shape, since we dont always pass batch size number of obs/act
normalized_obs0_flat = tf.reshape(normalized_obs0,
[-1, self.obs_shape_prod])
normalized_obs1_flat = tf.reshape(normalized_obs1,
[-1, self.obs_shape_prod])
actions_t_flat = tf.reshape(actions_t, [-1, self.act_shape_prod])
# Return normalization.
# TODO: update this to handle multiple agents if required
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
# Each agents gets its own observation
self.actor_tf = actor(normalized_act_obs0[self.agent_index])
self.target_actor_tf = target_actor(
normalized_act_obs1[self.agent_index])
# Critic gets all observations
self.normalized_critic_tf = critic(normalized_obs0_flat,
actions_t_flat)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
# need to provide critic() with all actions
act_input_n = self.actions + [] # copy actions
act_input_n[
self.
agent_index] = self.actor_tf # update current agent action using its actor
act_input_n_t = tf.transpose(act_input_n, perm=[1, 0, 2])
act_input_n_t_flat = tf.reshape(act_input_n_t,
[-1, self.act_shape_prod])
self.normalized_critic_with_actor_tf = critic(normalized_obs0_flat,
act_input_n_t_flat,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
# we need to use actions for all agents
target_act_input_n = self.actions + [] # copy actions
target_act_input_n[
self.
agent_index] = self.target_actor_tf # update current agent action using its target actor
target_act_input_n_t = tf.transpose(target_act_input_n, perm=[1, 0, 2])
target_act_input_n_t_flat = tf.reshape(target_act_input_n_t,
[-1, self.act_shape_prod])
Q_obs1 = denormalize(
target_critic(normalized_obs1_flat, target_act_input_n_t_flat),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
# param noise is added to actor; hence obs for current agent is required
self.setup_param_noise(normalized_act_obs0[self.agent_index])
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.initial_state = None # recurrent architectures not supported yet
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if var.name.endswith('/w:0') and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
# TODO: need to provide all observations to compute q
def step(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
# feed_dict={ph: [data] for ph, data in zip(self.obs0, obs)}
# feed_dict = {self.obs0: [obs]}
# Get the normalized obs first
# norm_obs0 = self.sess.run(self.norm_obs0, feed_dict=feed_dict)
# use the normalized obs for training
# feed_dict = {ph: data for ph, data in zip(self.norm_obs0_ph, norm_obs0)}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action[0].shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action[0], q, None, None
# TODO: test this
# Computing this every time step may slow things
def get_q_value(self, obs_n, act_n):
# assuming computing q value for one state; hence need [] around data
feed_dict = {ph: [data] for ph, data in zip(self.obs0, obs_n)}
act_dict = {ph: [data] for ph, data in zip(self.actions, act_n)}
feed_dict.update(act_dict)
q = self.sess.run(self.critic_with_actor_tf, feed_dict=feed_dict)
return q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
# print(action)
B = obs0.shape[0]
a_idx = self.agent_index
for b in range(B):
self.memory.append(obs0[b][a_idx], action[b][a_idx],
reward[b][a_idx], obs1[b][a_idx],
terminal1[b][a_idx])
# NOTE: calling update for each agent is ok, since the mean and std are uneffected
# this is because the same obs are repeated num_agent times, which dont affect value
if self.normalize_observations:
# provide full obs for obs_rms update
obs0_shape = (len(obs0[b]), ) + obs0[b][a_idx].shape
assert obs0_shape == (self.num_agents, ) + obs0[b][a_idx].shape
self.obs_rms.update(np.array([obs0[b]]))
# TODO: not using this right now
def update_obs_rms(self, obs0):
if not self.normalize_observations:
return
B = obs0.shape[0]
for b in range(B):
# provide full obs for obs_rms update
self.obs_rms.update(np.array([obs0[b]]))
return
def train(self, agents):
# generate indices to access batches from all agents
replay_sample_index = self.memory.generate_index(self.batch_size)
# collect replay sample from all agents
obs0_n = []
obs1_n = []
rewards_n = []
act_n = []
terminals1_n = []
for i in range(self.num_agents):
# Get a batch.
batch = agents[i].memory.sample(batch_size=self.batch_size,
index=replay_sample_index)
obs0_n.append(batch['obs0'])
obs1_n.append(batch['obs1'])
act_n.append(batch['actions'])
# rewards_n.append(batch['rewards'])
# terminals1_n.append(batch['terminals1'])
batch = self.memory.sample(batch_size=self.batch_size,
index=replay_sample_index)
# fill placeholders in obs1 with corresponding obs from each agent's replay buffer
# self.obs1 and obs1_n are lists of size num_agents
# feed_dict={ph: data for ph, data in zip(self.obs1, obs1_n)}
feed_dict = {self.obs1: obs1_n}
# TODO: find a better way to do this
# Get the normalized obs first
# norm_obs1 = self.sess.run(self.norm_obs1, feed_dict=feed_dict)
# use the normalized obs for training
# feed_dict = {self.norm_obs1_ph: norm_obs1}
# feed_dict = {ph: data for ph, data in zip(self.norm_obs1_ph, norm_obs1)}
# actions required for critic
act_dict = {ph: data for ph, data in zip(self.actions, act_n)}
feed_dict.update(act_dict)
feed_dict.update({self.rewards: batch['rewards']})
feed_dict.update(
{self.terminals1: batch['terminals1'].astype('float32')})
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict=feed_dict)
# old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q, feed_dict=feed_dict)
# target_Q = self.sess.run(self.target_Q, feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
# generate feed_dict for multiple observations and actions
# feed_dict={ph: data for ph, data in zip(self.obs0, obs0_n)}
feed_dict = {self.obs0: obs0_n}
# Get the normalized obs first
# norm_obs0 = self.sess.run(self.norm_obs0, feed_dict=feed_dict)
# use the normalized obs for training
# feed_dict = {self.norm_obs0_ph: norm_obs0}
# feed_dict = {ph: data for ph, data in zip(self.norm_obs0_ph, norm_obs0)}
# act_dict={ph: data for ph, data in zip(self.actions, act_n)}
feed_dict.update(act_dict)
feed_dict.update({self.critic_target: target_Q})
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops, feed_dict=feed_dict)
# actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
# self.obs0: batch['obs0'],
# self.actions: batch['actions'],
# self.critic_target: target_Q,
# })
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
def agent_initialize(self, sess):
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
# setup saving and loading functions
self.save = functools.partial(save_variables, sess=sess)
self.load = functools.partial(load_variables, sess=sess)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self, agents):
if self.stats_sample is None:
replay_sample_index = self.memory.generate_index(self.batch_size)
# collect replay sample from all agents
obs0_n, act_n = [], []
for i in range(self.num_agents):
batch = agents[i].memory.sample(batch_size=self.batch_size,
index=replay_sample_index)
obs0_n.append(batch['obs0'])
act_n.append(batch['actions'])
# generate feed_dict for multiple observations and actions
# feed_dict={ph: data for ph, data in zip(self.obs0, obs0_n)}
feed_dict = {self.obs0: obs0_n}
# Get the normalized obs first
# norm_obs0 = self.sess.run(self.norm_obs0, feed_dict=feed_dict)
# use the normalized obs for training
# feed_dict = {self.norm_obs0_ph: norm_obs0}
# feed_dict = {ph: data for ph, data in zip(self.norm_obs0_ph, norm_obs0)}
actions_dict = {ph: data for ph, data in zip(self.actions, act_n)}
feed_dict.update(actions_dict)
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = feed_dict
values = self.sess.run(self.stats_ops, feed_dict=self.stats_sample)
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self, agents):
try:
from mpi4py import MPI
except ImportError:
MPI = None
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
replay_sample_index = self.memory.generate_index(self.batch_size)
obs0_n = []
for i in range(self.num_agents):
batch = agents[i].memory.sample(batch_size=self.batch_size,
index=replay_sample_index)
obs0_n.append(batch['obs0'])
# feed_dict={ph: data for ph, data in zip(self.obs0, obs0_n)}
feed_dict = {self.obs0: obs0_n}
# Get the normalized obs first
# norm_obs0 = self.sess.run(self.norm_obs0, feed_dict=feed_dict)
# use the normalized obs for training
# feed_dict = {self.norm_obs0_ph: norm_obs0}
# feed_dict = {ph: data for ph, data in zip(self.norm_obs0_ph, norm_obs0)}
feed_dict.update(
{self.param_noise_stddev: self.param_noise.current_stddev})
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict=feed_dict)
# distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
# self.obs0: batch['obs0'],
# self.param_noise_stddev: self.param_noise.current_stddev,
# })
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-1., 1.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
adaptive_param_noise=True,
adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.saver = self.get_saver()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def get_saver(self):
exclude = [
'Variable/ExponentialMovingAverage:0',
'Variable/Adam:0',
'Variable/Adam_1:0',
'Variable_8/ExponentialMovingAverage:0',
'Variable_8/Adam:0'
'Variable/Adam_1:0',
]
nodes = tf.trainable_variables()
mapping = {
var.name.split(':')[0]: var
for var in nodes if var.name not in exclude
}
return tf.train.Saver()
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def save_model(self, path, episode):
filename = os.path.join(path, "model.ckpt")
self.saver.save(self.sess, filename, episode)
print("Saved model to ", filename)
def restore_model(self, path):
try:
checkpoint = tf.train.latest_checkpoint(path)
self.saver.restore(self.sess, checkpoint)
print("Restored model from ", checkpoint)
except Exception as e:
print(e)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def q(self, obs):
"""Compute the q value for some observation"""
return self.sess.run(self.critic_with_actor_tf,
feed_dict={self.obs0: obs})
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = mpi_mean(distance)
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class DDPG(object):
def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None,
action_noise=None, gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False,
normalize_observations=True, batch_size=128, observation_range=(-5., 5.),
action_range=(-1., 1.), return_range=(-np.inf, np.inf),
adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
self.obs0 = tf.placeholder(tf.float32, shape=(None, ) + observation_shape, name="obs0")
self.obs1 = tf.placeholder(tf.float32, shape=(None, ) + observation_shape, name="obs1")
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name="terminals1")
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name="rewards")
self.actions = tf.placeholder(tf.float32, shape=(None, ) + action_shape, name="actions")
self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name="critic_target")
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name="param_noise_stddev")
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
if self.normalize_observations:
with tf.variable_scope("obs_rms"):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
if self.normalize_returns:
with tf.variable_scope("ret_rms"):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
target_actor = copy(actor)
target_actor.name = "target_actor"
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = "target_critic"
self.target_critic = target_critic
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf,
self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ["ret_rms_mean", "ret_rms_std"]
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ["obs_rms_mean", "obs_rms_std"]
ops += [tf.reduce_mean(self.critic_tf)]
names += ["reference_Q_mean"]
ops += [reduce_std(self.critic_tf)]
names += ["reference_Q_std"]
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ["reference_actor_Q_mean"]
ops += [reduce_std(self.critic_with_actor_tf)]
names += ["reference_actor_Q_std"]
ops += [tf.reduce_mean(self.actor_tf)]
names += ["reference_action_mean"]
ops += [reduce_std(self.actor_tf)]
names += ["reference_action_std"]
if self.param_noise:
ops += [tf.reduce_mean(self.pertubed_actor_tf)]
names += ["reference_perturbed_action_mean"]
ops += [reduce_std(self.pertubed_actor_tf)]
names += ["reference_perturbed_action_std"]
self.stats_ops = ops
self.stats_names = names
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_popart(self):
self.old_std = tf.placeholder(tf.float32, shape=[1], name="old_std")
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name="old_mean")
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert "kernel" in M.name
assert "bias" in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean)/ new_std)]
def setup_critic_optimizer(self):
logger.info("setting up critic optimizer")
normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms),
self.return_range[0], self.return_range[1])
self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0:
critic_reg_vars = [var for var in self.critic.trainable_vars if "kernel" in var.name and "output" not in var.name]
for var in critic_reg_vars:
logger.info(" regularizing: {}".format(var.name))
logger.info(" applying l2 regularization with {}".format(self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x *y, shape) for shape in critic_shapes])
logger.info(" critic shapes: {}".format(critic_shapes))
logger.info(" critic params: {}".format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars, beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_actor_optimizer(self):
logger.info("setting up actor optimizer")
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(" actor shapes: {}".format(actor_shapes))
logger.info(" actor params: {}".format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars, beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
param_noise_actor = copy(self.actor)
param_noise_actor.name = "param_noise_actor"
self.pertubed_actor_tf = param_noise_actor(normalized_obs0)
logger.info("setting up param noise")
self.pertub_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = "adaptive_param_noise_actor"
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor,
adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def reset(self):
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.pertub_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev
})
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.pertubed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward = self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={self.param_noise_stddev: self.param_noise.current_stddev})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0 : batch["obs0"],
self.param_noise_stddev: self.param_noise.current_stddev
})
mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def train(self):
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std,
self.target_Q], feed_dict = {
self.obs1: batch["obs1"],
self.rewards: batch["rewards"],
self.terminals1: batch["terminals1"].astype("float32")
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
else:
target_Q = self.sess.run(self.target_Q, feed_dict={
self.obs1: batch["obs1"],
self.rewards: batch["rewards"],
self.terminals1: batch["terminals1"].astype("float32")
})
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch["obs0"],
self.actions: batch["actions"],
self.critic_target: target_Q
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample["obs0"],
self.actions: self.stats_sample["actions"],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
class DDPG(object):
def __init__(self, **params):
for k in params:
setattr(self, k, params[k])
self.init_args = copy(params)
if self.her:
# self.obs_to_goal = None
# self.goal_idx = None
# self.reward_fn = None
self.memory = HERBuffer(limit=int(self.buffer_size),
action_shape=self.action_shape,
observation_shape=self.observation_shape,
obs_to_goal=self.obs_to_goal,
goal_slice=self.goal_idx,
reward_fn=self.reward_fn)
else:
self.memory = Memory(limit=int(self.buffer_size),
action_shape=self.action_shape,
observation_shape=self.observation_shape)
self.critic = Critic(layer_norm=self.layer_norm)
self.actor = Actor(self.action_shape[-1], layer_norm=self.layer_norm)
self.action_noise = NormalActionNoise(mu=np.zeros(self.action_shape),
sigma=float(self.noise_sigma) *
np.ones(self.action_shape))
self.param_noise = None
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + self.observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + self.observation_shape,
name='obs1')
# self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + self.action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=self.observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(self.actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(self.critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = self.actor(normalized_obs0)
self.normalized_critic_tf = self.critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = self.critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
# self.target_Q = self.rewards + (1. - self.terminals1) * self.gamma * Q_obs1
self.target_Q = self.rewards + self.gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(
self.target_Q,
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = mpi_mean(distance)
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
self.flush()
def flush(self):
if self.her:
self.memory.flush()
def get_save_tf(self):
all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
return self.sess.run(all_variables)
def restore_tf(self, save):
all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
restore_ops = []
for x, y in zip(all_variables, save):
restore_ops.append(tf.assign(x, y))
self.sess.run(restore_ops)
def __getstate__(self):
exclude_vars = set(["env"])
args = {}
for k in self.init_args:
if k not in exclude_vars:
args[k] = self.init_args[k]
return {'tf': self.get_save_tf(), 'init': args}
def __setstate__(self, state):
self.__init__(**state['init'])
self.sess = tf.InteractiveSession(
) # for now just make ourself a session
self.sess.run(tf.global_variables_initializer())
self.restore_tf(state['tf'])
self.actor_optimizer.sync()
self.critic_optimizer.sync()
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
dropout_on_v,
dropout_tau_V,
override_reg,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=64,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
adaptive_param_noise=True,
adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
### MAIN CHANGES
self.override_reg = override_reg
self.dropout_on_v = dropout_on_v
self.dropout_tau_V = dropout_tau_V
self.observation_shape = observation_shape
self.b = tf.placeholder(tf.float32)
### END
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
### MAIN CHANGES
## Q(s,a)
if self.dropout_on_v is not None:
self.normalized_critic_tf, _, self.normalized_critic_tf_mc = critic(
normalized_obs0, self.actions)
else:
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
## Q(s, mu(s))
if self.dropout_on_v is not None:
self.normalized_critic_with_actor_tf, self.normalized_critic_with_actor_tf_avg, _ = critic(
normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf_avg,
self.return_range[0], self.return_range[1]),
self.ret_rms)
else:
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
self.Q = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
if dropout_on_v is not None:
Q_obs1, _, _ = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
else:
Q_obs1 = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
### END OF CHANGES
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
if replace_masks:
update_dropout_masks([
x for x in self.critic.vars
if 'dropout' in x.name and 'mask' in x.name
],
self.critic.keep_prob,
execute=False)
update_dropout_masks([
x for x in self.target_critic.vars
if 'dropout' in x.name and 'mask' in x.name
],
self.critic.keep_prob,
execute=False)
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.trainable_vars, self.target_critic.trainable_vars,
self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
### MAIN CHANGES
### eq 10 of the alpha black box dropout
if self.dropout_on_v is not None:
self.alpha = 0.5
x = normalized_critic_target_tf
self.flat = self.normalized_critic_tf_mc
flat_stacked = tf.stack(self.flat) # K x M x outsize
# M x B X outsize
sumsq = U.sum(tf.square(x - flat_stacked), -1)
sumsq *= (-.5 * self.alpha * self.dropout_tau_V)
self.critic_loss = (-1.0 * self.alpha**-1.0) * logsumexp(sumsq, 0)
self.l2_value = self.critic.keep_prob * float(
self.batch_size) / (float(self.memory.nb_entries) + 1)
self.critic_l2_reg = tf.Variable(self.l2_value, trainable=False)
else:
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf -
normalized_critic_target_tf))
if self.override_reg is not None:
self.critic_l2_reg = self.override_reg
### END OF CHANGES
if self.override_reg is not None:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
### MAIN CHANGES
action, q = self.sess.run([actor_tf, self.Q], feed_dict=feed_dict)
# action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
### END OF CHANGES
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
if replace_masks:
update_dropout_masks([
x for x in self.critic.vars
if 'dropout' in x.name and 'mask' in x.name
], self.critic.keep_prob)
update_dropout_masks([
x for x in self.target_critic.vars
if 'dropout' in x.name and 'mask' in x.name
], self.critic.keep_prob)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = mpi_mean(distance)
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class TransitionClassifier(object):
def __init__(self,
ob_size,
ac_size,
hidden_size=100,
log_reward=False,
entcoeff=0.001,
scope="adversary",
dyn_norm=True):
self.scope = scope
self.ob_size = ob_size
self.ac_size = ac_size
# self.input_size = ob_size + ac_size
self.hidden_size = hidden_size
self.log_reward = log_reward
self.dyn_norm = dyn_norm
self.build_ph()
# Build grpah
generator_logits = self.build_graph(self.generator_obs_ph,
self.generator_acs_ph)
expert_logits = self.build_graph(self.expert_obs_ph,
self.expert_acs_ph)
# Build accuracy
generator_acc = tf.reduce_mean(
tf.cast(tf.nn.sigmoid(generator_logits) < 0.5, tf.float32))
expert_acc = tf.reduce_mean(
tf.cast(tf.nn.sigmoid(expert_logits) > 0.5, tf.float32))
# Build regression loss
# let x = logits, z = targets.
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
generator_loss = tf.nn.sigmoid_cross_entropy_with_logits(
logits=generator_logits, labels=tf.zeros_like(generator_logits))
generator_loss = tf.reduce_mean(generator_loss)
expert_loss = tf.nn.sigmoid_cross_entropy_with_logits(
logits=expert_logits, labels=tf.ones_like(expert_logits))
expert_loss = tf.reduce_mean(expert_loss)
# Build entropy loss
logits = tf.concat([generator_logits, expert_logits], 0)
entropy = tf.reduce_mean(logit_bernoulli_entropy(logits))
entropy_loss = -entcoeff * entropy
# Loss + Accuracy terms
self.losses = [
generator_loss, expert_loss, entropy, entropy_loss, generator_acc,
expert_acc
]
self.loss_name = [
"generator_loss", "expert_loss", "entropy", "entropy_loss",
"generator_acc", "expert_acc"
]
self.total_loss = generator_loss + expert_loss + entropy_loss
# Build Reward for policy
if log_reward:
reward_op = -tf.log(1 - tf.nn.sigmoid(generator_logits) + 1e-8)
else:
reward_op = tf.nn.sigmoid(generator_logits)
self.reward = U.function(
[self.generator_obs_ph, self.generator_acs_ph], reward_op)
lr = tf.placeholder(tf.float32, None)
self.trainer = tf.train.AdamOptimizer(learning_rate=lr)
gvs = self.trainer.compute_gradients(self.total_loss,
self.get_trainable_variables())
self._train = U.function([
self.generator_obs_ph, self.generator_acs_ph, self.expert_obs_ph,
self.expert_acs_ph, lr
],
self.losses,
updates=[self.trainer.apply_gradients(gvs)])
def build_ph(self):
self.generator_obs_ph = tf.placeholder(tf.float32,
(None, self.ob_size),
name="observations_ph")
self.generator_acs_ph = tf.placeholder(tf.float32,
(None, self.ac_size),
name="actions_ph")
self.expert_obs_ph = tf.placeholder(tf.float32, (None, self.ob_size),
name="expert_observations_ph")
self.expert_acs_ph = tf.placeholder(tf.float32, (None, self.ac_size),
name="expert_actions_ph")
def build_graph(self, obs_ph, acs_ph):
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
with tf.variable_scope("obfilter"):
self.obs_rms = RunningMeanStd(shape=[self.ob_size])
obs = normalize(obs_ph, self.obs_rms)
_input = tf.concat(
[obs, acs_ph],
axis=1) # concatenate the two input -> form a transition
p_h1 = tf.contrib.layers.fully_connected(_input,
self.hidden_size,
activation_fn=tf.nn.tanh)
p_h2 = tf.contrib.layers.fully_connected(p_h1,
self.hidden_size,
activation_fn=tf.nn.tanh)
logits = tf.contrib.layers.fully_connected(p_h2,
1,
activation_fn=None)
return logits
def get_trainable_variables(self):
return tf.trainable_variables(self.scope)
def get_reward(self, obs, acs):
return np.squeeze(self.reward(obs, acs))
def build_reward_op(self, obs_ph, acs_ph):
logits = self.build_graph(obs_ph, acs_ph)
if self.log_reward:
return -tf.log(1 - tf.nn.sigmoid(logits) + 1e-8)
return tf.nn.sigmoid(logits)
def set_expert_data(self, data):
self.data = Dataset(data, deterministic=False)
def train(self, rl_ob, rl_ac, steps=1, lr=3e-4):
n = rl_ob.shape[0]
loss_buf = []
batch_size = rl_ob.shape[0] // steps
for batch in iterbatches([rl_ob, rl_ac],
include_final_partial_batch=False,
batch_size=batch_size):
exp_ob, exp_ac = self.data.next_batch(batch_size)
if self.obs_rms and self.dyn_norm:
self.obs_rms.update(np.concatenate([exp_ob, rl_ob], axis=0))
loss_buf.append(self._train(*batch, exp_ob, exp_ac, lr))
logger.info(fmt_row(13, self.loss_name))
logger.info(fmt_row(13, np.mean(loss_buf, axis=0)))
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
"""
Deep Deterministic Policy Gradien (DDPG) model
DDPG: https://arxiv.org/pdf/1509.02971.pdf
:param actor: (TensorFlow Tensor) the actor model
:param critic: (TensorFlow Tensor) the critic model
:param memory: (Memory) the replay buffer
:param observation_shape: (tuple) the observation space
:param action_shape: (tuple) the action space
:param param_noise: (AdaptiveParamNoiseSpec) the parameter noise type (can be None)
:param action_noise: (ActionNoise) the action noise type (can be None)
:param gamma: (float) the discount rate
:param tau: (float) the soft update coefficient (keep old values, between 0 and 1)
:param normalize_returns: (bool) should the critic output be normalized
:param enable_popart: (bool) enable pop-art normalization of the critic output
(https://arxiv.org/pdf/1602.07714.pdf)
:param normalize_observations: (bool) should the observation be normalized
:param batch_size: (int) the size of the batch for learning the policy
:param observation_range: (tuple) the bounding values for the observation
:param action_range: (tuple) the bounding values for the actions
:param return_range: (tuple) the bounding values for the critic output
:param critic_l2_reg: (float) l2 regularizer coefficient
:param actor_lr: (float) the actor learning rate
:param critic_lr: (float) the critic learning rate
:param clip_norm: (float) clip the gradients (disabled if None)
:param reward_scale: (float) the value the reward should be scaled by
"""
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.target_init_updates = None
self.target_soft_updates = None
self.critic_loss = None
self.critic_grads = None
self.critic_optimizer = None
self.sess = None
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
q_obs1 = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
self.target_q = self.rewards + (1. - self.terminals1) * gamma * q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
"""
set the target update operations
"""
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
"""
set the parameter noise operations
:param normalized_obs0: (TensorFlow Tensor) the normalized observation
"""
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
"""
setup the optimizer for the actor
"""
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = tf_util.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
"""
setup the optimizer for the critic
"""
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = tf_util.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
"""
setup pop-art normalization of the critic output
See https://arxiv.org/pdf/1602.07714.pdf for details.
Preserving Outputs Precisely, while Adaptively Rescaling Targets”.
"""
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_q_outputs_op = []
for out_vars in [
self.critic.output_vars, self.target_critic.output_vars
]:
assert len(out_vars) == 2
# wieght and bias of the last layer
weight, bias = out_vars
assert 'kernel' in weight.name
assert 'bias' in bias.name
assert weight.get_shape()[-1] == 1
assert bias.get_shape()[-1] == 1
self.renormalize_q_outputs_op += [
weight.assign(weight * self.old_std / new_std)
]
self.renormalize_q_outputs_op += [
bias.assign(
(bias * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
"""
setup the running means and std of the inputs and outputs of the model
"""
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def policy(self, obs, apply_noise=True, compute_q=True):
"""
Get the actions and critic output, from a given observation
:param obs: ([float] or [int]) the observation
:param apply_noise: (bool) enable the noise
:param compute_q: (bool) compute the critic output
:return: ([float], float) the action and critic value
"""
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_q:
action, q_value = self.sess.run(
[actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q_value = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q_value
def store_transition(self, obs0, action, reward, obs1, terminal1):
"""
Store a transition in the replay buffer
:param obs0: ([float] or [int]) the last observation
:param action: ([float]) the action
:param reward: (float] the reward
:param obs1: ([float] or [int]) the current observation
:param terminal1: (bool) is the episode done
"""
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
"""
run a step of training from batch
:return: (float, float) critic loss, actor loss
"""
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_q.flatten())
self.sess.run(self.renormalize_q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
else:
target_q = self.sess.run(self.target_q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_q,
})
self.actor_optimizer.update(actor_grads, learning_rate=self.actor_lr)
self.critic_optimizer.update(critic_grads,
learning_rate=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
"""
initialize the model parameters and optimizers
:param sess: (TensorFlow Session) the current TensorFlow session
"""
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
"""
run target soft update operation
"""
self.sess.run(self.target_soft_updates)
def get_stats(self):
"""
Get the mean and standard deviation of the model's inputs and outputs
:return: (dict) the means and stds
"""
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
"""
calculate the adaptation for the parameter noise
:return: (float) the mean distance for the parameter noise
"""
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
"""
Reset internal state after an episode is complete.
"""
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class DDPG(object):
def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
batch_size=128, observation_range=(-1., 1.), action_range= [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], return_range=(-np.inf, np.inf),
adaptive_param_noise=True, critic_l2_reg=0.,
adaptive_param_noise_policy_threshold=.1, actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1., restore=False):
# Inputs.
# self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs0 = tf.placeholder(tf.float32, shape=(None, observation_shape), name='obs0')
# self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
self.obs1 = tf.placeholder(tf.float32, shape=(None, observation_shape), name='obs1')
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(tf.float32, shape=(None, action_shape), name='actions')
self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
"""Filewriter summary"""
monitor_directory = os.path.join("Experiment_data")
self.summary_dir = os.path.join(monitor_directory, "summary")
# if restore:
# dirname = 'run20' # The last name
# self.summary_dir = os.path.join(self.summary_dir, dirname)
# else:
self.summary_dir = utils.new_summary_dir(self.summary_dir)
# record the detailed parameters
utils.log_params(self.summary_dir, {
"actor learning rate": self.actor_lr,
"critic learning rate": self.critic_lr,
"batch size": self.batch_size,
"actor update rate": self.tau,
"critic update rate": self.tau,
"action noise": self.action_noise,
"param noise": self.param_noise,
"reward function": 'General reward function',
"result_function": 'The second 100'
})
self.merged = tf.summary.merge_all()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None ## 确保假设完全正确
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0: batch['obs0'],
self.param_noise_stddev: self.param_noise.current_stddev,
})
mean_distance = mpi_mean(distance)
self.param_noise.adapt(mean_distance)
return mean_distance
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.multiply(action, self.action_range)
# action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def save_data(self):
self.memory.save_data()
def train(self, dec_actor_lr, dec_critic_lr):
# change the learning rate
self.actor_lr = dec_actor_lr
self.critic_lr = dec_critic_lr
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
self.old_std : np.array([old_std]),
self.old_mean : np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q, feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
## wirte the graph
self.summary_writer = tf.summary.FileWriter(self.summary_dir, self.sess.graph)
def restore_model(self, model_directory, saver, sess):
ckpt = tf.train.get_checkpoint_state(model_directory)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
self.sess = sess
logger.info('Load the saved model from the directory!!!')
self.summary_writer = tf.summary.FileWriter(self.summary_dir)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def feedback_adptive_explore(self):
self.param_noise.adapt_variance()
def ou_adaptive_explore(self):
self.action_noise.adapt_decrease()
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
def log_scalar(self, name, value, index):
summary_value = summary_pb2.Summary.Value(tag=name, simple_value=value)
summary_2 = summary_pb2.Summary(value=[summary_value])
self.summary_writer.add_summary(summary_2, global_step=index)
class Model(object):
def __init__(self, sess, policy, dynamics, ob_space, ac_space, nenvs,
nsteps, ent_coef, q_coef, gamma, max_grad_norm, lr,
rprop_alpha, rprop_epsilon, total_timesteps, lrschedule, c,
trust_region, alpha, delta, scope, goal_shape, residual):
self.sess = sess
self.nenv = nenvs
self.residual = residual
self.goal_shape = goal_shape
self.goal_as_image = goal_as_image = len(goal_shape) == 3
if self.goal_as_image:
assert self.goal_shape == ob_space.shape
else:
logger.info("normalize goal using RunningMeanStd")
with tf.variable_scope("RunningMeanStd", reuse=tf.AUTO_REUSE):
self.goal_rms = RunningMeanStd(epsilon=1e-4,
shape=self.goal_shape)
nact = ac_space.n
nbatch = nenvs * nsteps
eps = 1e-6
self.dynamics = dynamics
self.scope = scope
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
self.A = tf.placeholder(tf.int32, [nbatch],
name="action") # actions
self.D = tf.placeholder(tf.float32, [nbatch],
name="dones") # dones
self.R = tf.placeholder(tf.float32, [nbatch],
name="rewards") # rewards, not returns
self.MU = tf.placeholder(tf.float32, [nbatch, nact],
name="mus") # mu's
self.LR = tf.placeholder(tf.float32, [], name="lr")
self.V_NEXT = tf.placeholder(tf.float32, [nbatch], name="v_next")
step_ob_placeholder = tf.placeholder(ob_space.dtype,
(nenvs, ) + ob_space.shape,
"step_ob")
if self.dynamics.dummy:
step_goal_placeholder, concat_on_latent, step_goal_encoded = None, None, None
else:
if goal_as_image:
step_goal_placeholder = tf.placeholder(
ob_space.dtype, (nenvs, ) + ob_space.shape,
"step_goal")
concat_on_latent, train_goal_encoded, step_goal_encoded = False, None, None
else:
step_goal_placeholder = tf.placeholder(
tf.float32, (nenvs, ) + goal_shape, "step_goal")
step_goal_encoded = tf.clip_by_value(
(step_goal_placeholder - self.goal_rms.mean) /
self.goal_rms.std, -5., 5.)
train_ob_placeholder = tf.placeholder(
ob_space.dtype, (nenvs * nsteps, ) + ob_space.shape,
"train_ob")
if self.dynamics.dummy:
train_goal_placeholder, concat_on_latent, train_goal_encoded = None, None, None
else:
if goal_as_image:
train_goal_placeholder = tf.placeholder(
ob_space.dtype, (nenvs * nsteps, ) + ob_space.shape,
"train_goal")
concat_on_latent, train_goal_encoded = False, None
else:
train_goal_placeholder = tf.placeholder(
tf.float32, (nenvs * nsteps, ) + goal_shape,
"train_goal")
concat_on_latent = True
train_goal_encoded = tf.clip_by_value(
(train_goal_placeholder - self.goal_rms.mean) /
self.goal_rms.std, -5., 5.)
self.step_model = policy(nbatch=nenvs,
nsteps=1,
observ_placeholder=step_ob_placeholder,
sess=self.sess,
goal_placeholder=step_goal_placeholder,
concat_on_latent=concat_on_latent,
goal_encoded=step_goal_encoded)
self.train_model = policy(nbatch=nbatch,
nsteps=nsteps,
observ_placeholder=train_ob_placeholder,
sess=self.sess,
goal_placeholder=train_goal_placeholder,
concat_on_latent=concat_on_latent,
goal_encoded=train_goal_encoded)
variables = find_trainable_variables
self.params = params = variables(scope)
logger.info(
"========================== {} =============================".
format(scope))
for var in params:
logger.info(var)
logger.info(
"========================== {} =============================\n".
format(scope))
logger.info(
"======================={}: Aux & Dyna =========================".
format(scope))
for var in self.dynamics.params:
logger.info(var)
logger.info(
"======================={}: Aux & Dyna =========================\n"
.format(scope))
# create polyak averaged model
ema = tf.train.ExponentialMovingAverage(alpha)
ema_apply_op = ema.apply(params)
# print("========================== Ema =============================")
def custom_getter(getter, *args, **kwargs):
v = ema.average(getter(*args, **kwargs))
# print(v.name)
return v
# print("========================== Ema =============================")
with tf.variable_scope(scope, custom_getter=custom_getter, reuse=True):
self.polyak_model = policy(nbatch=nbatch,
nsteps=nsteps,
observ_placeholder=train_ob_placeholder,
goal_placeholder=train_goal_placeholder,
sess=self.sess,
concat_on_latent=concat_on_latent,
goal_encoded=train_goal_encoded)
# Notation: (var) = batch variable, (var)s = seqeuence variable, (var)_i = variable index by action at step i
# action probability distributions according to self.train_model, self.polyak_model and self.step_model
# poilcy.pi is probability distribution parameters; to obtain distribution that sums to 1 need to take softmax
train_model_p = tf.nn.softmax(self.train_model.pi)
polyak_model_p = tf.nn.softmax(self.polyak_model.pi)
self.step_model_p = tf.nn.softmax(self.step_model.pi)
v = self.v = tf.reduce_sum(train_model_p * self.train_model.q,
axis=-1) # shape is [nenvs * (nsteps)]
# strip off last step
f, f_pol, q = map(lambda var: strip(var, nenvs, nsteps),
[train_model_p, polyak_model_p, self.train_model.q])
# Get pi and q values for actions taken
f_i = get_by_index(f, self.A)
q_i = get_by_index(q, self.A)
# Compute ratios for importance truncation
rho = f / (self.MU + eps)
rho_i = get_by_index(rho, self.A)
# Calculate Q_retrace targets
qret = q_retrace(self.R, self.D, q_i, self.V_NEXT, rho_i, nenvs,
nsteps, gamma)
# Calculate losses
# Entropy
# entropy = tf.reduce_mean(strip(self.train_model.pd.entropy(), nenvs, nsteps))
entropy = tf.reduce_mean(cat_entropy_softmax(f))
# Policy Graident loss, with truncated importance sampling & bias correction
v = strip(v, nenvs, nsteps, True)
check_shape([qret, v, rho_i, f_i], [[nenvs * nsteps]] * 4)
check_shape([rho, f, q], [[nenvs * nsteps, nact]] * 2)
# Truncated importance sampling
adv = qret - v
logf = tf.log(f_i + eps)
gain_f = logf * tf.stop_gradient(
adv * tf.minimum(c, rho_i)) # [nenvs * nsteps]
loss_f = -tf.reduce_mean(gain_f)
# Bias correction for the truncation
adv_bc = (q - tf.reshape(v, [nenvs * nsteps, 1])
) # [nenvs * nsteps, nact]
logf_bc = tf.log(f + eps) # / (f_old + eps)
check_shape([adv_bc, logf_bc], [[nenvs * nsteps, nact]] * 2)
gain_bc = tf.reduce_sum(
logf_bc *
tf.stop_gradient(adv_bc * tf.nn.relu(1.0 - (c / (rho + eps))) * f),
axis=1) # IMP: This is sum, as expectation wrt f
loss_bc = -tf.reduce_mean(gain_bc)
loss_policy = loss_f + loss_bc
# Value/Q function loss, and explained variance
check_shape([qret, q_i], [[nenvs * nsteps]] * 2)
ev = q_explained_variance(tf.reshape(q_i, [nenvs, nsteps]),
tf.reshape(qret, [nenvs, nsteps]))
loss_q = tf.reduce_mean(tf.square(tf.stop_gradient(qret) - q_i) * 0.5)
# Net loss
check_shape([loss_policy, loss_q, entropy], [[]] * 3)
# Goal loss
loss = loss_policy + q_coef * loss_q - ent_coef * entropy
if trust_region:
g = tf.gradients(-(loss_policy - ent_coef * entropy) * nsteps *
nenvs, f) # [nenvs * nsteps, nact]
# k = tf.gradients(KL(f_pol || f), f)
k = -f_pol / (
f + eps
) # [nenvs * nsteps, nact] # Directly computed gradient of KL divergence wrt f
k_dot_g = tf.reduce_sum(k * g, axis=-1)
adj = tf.maximum(0.0, (tf.reduce_sum(k * g, axis=-1) - delta) /
(tf.reduce_sum(tf.square(k), axis=-1) +
eps)) # [nenvs * nsteps]
# Calculate stats (before doing adjustment) for logging.
avg_norm_k = avg_norm(k)
avg_norm_g = avg_norm(g)
avg_norm_k_dot_g = tf.reduce_mean(tf.abs(k_dot_g))
avg_norm_adj = tf.reduce_mean(tf.abs(adj))
g = g - tf.reshape(adj, [nenvs * nsteps, 1]) * k
grads_f = -g / (
nenvs * nsteps
) # These are turst region adjusted gradients wrt f ie statistics of policy pi
grads_policy = tf.gradients(f, params, grads_f)
grads_q = tf.gradients(loss_q * q_coef, params)
# print("=========================== gards add ==============================")
grads = [
gradient_add(g1, g2, param)
for (g1, g2, param) in zip(grads_policy, grads_q, params)
]
# print("=========================== gards add ==============================\n")
avg_norm_grads_f = avg_norm(grads_f) * (nsteps * nenvs)
norm_grads_q = tf.global_norm(grads_q)
norm_grads_policy = tf.global_norm(grads_policy)
else:
grads = tf.gradients(loss, params)
if max_grad_norm is not None:
grads, norm_grads = tf.clip_by_global_norm(grads, max_grad_norm)
grads = list(zip(grads, params))
trainer = tf.train.RMSPropOptimizer(learning_rate=self.LR,
decay=rprop_alpha,
epsilon=rprop_epsilon)
_policy_opt_op = trainer.apply_gradients(grads)
if not self.dynamics.dummy:
_train_dynamics = trainer.minimize(self.dynamics.loss)
self.run_ops_dynamics = [
_train_dynamics,
self.dynamics.aux_loss,
self.dynamics.dyna_loss,
]
self.name_ops_dynamics = ["aux_loss", "dyna_loss"]
# so when you call _train, you first do the gradient step, then you apply ema
with tf.control_dependencies([_policy_opt_op]):
_train_policy = tf.group(ema_apply_op)
self.lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule)
# Ops/Summaries to run, and their names for logging
self.run_ops_policy = [
_train_policy, loss, loss_q, entropy, loss_policy, loss_f, loss_bc,
ev, norm_grads
]
self.names_ops_policy = [
'loss', 'loss_q', 'entropy', 'loss_policy', 'loss_f', 'loss_bc',
'explained_variance', 'norm_grads'
]
if trust_region:
self.run_ops_policy = self.run_ops_policy + [
norm_grads_q, norm_grads_policy, avg_norm_grads_f, avg_norm_k,
avg_norm_g, avg_norm_k_dot_g, avg_norm_adj
]
self.names_ops_policy = self.names_ops_policy + [
'norm_grads_q', 'norm_grads_policy', 'avg_norm_grads_f',
'avg_norm_k', 'avg_norm_g', 'avg_norm_k_dot_g', 'avg_norm_adj'
]
self.names_ops_policy = [
scope + "_" + x for x in self.names_ops_policy
] # scope as prefix
self.save = functools.partial(save_variables,
sess=self.sess,
variables=params)
self.initial_state = self.step_model.initial_state
tf.global_variables_initializer().run(session=self.sess)
def train_policy(self,
obs,
next_obs,
actions,
rewards,
dones,
mus,
states,
masks,
steps,
goal_obs,
verbose=False):
cur_lr = self.lr.value_steps(steps)
# 1. calculate v_{t+1} using obs_{t+1} and g_t
td_map = {self.train_model.X: next_obs}
if not self.dynamics.dummy:
assert hasattr(self.train_model, "goals")
if self.residual:
td_map[self.train_model.goals] = goal_obs - next_obs
else:
td_map[self.train_model.goals] = goal_obs
v_next = self.sess.run(self.v, feed_dict=td_map)
# 2. use obs_t, goal_t, v_{t+1} to train policy
td_map = {
self.train_model.X: obs,
self.polyak_model.X: obs,
self.A: actions,
self.R: rewards,
self.D: dones,
self.MU: mus,
self.LR: cur_lr,
self.V_NEXT: v_next
}
if not self.dynamics.dummy:
assert hasattr(self.train_model, "goals")
assert hasattr(self.polyak_model, "goals")
if hasattr(self, "goal_rms"):
self.goal_rms.update(goal_obs)
if self.residual:
td_map[self.train_model.goals] = goal_obs - obs
td_map[self.polyak_model.goals] = goal_obs - obs
else:
td_map[self.train_model.goals] = goal_obs
td_map[self.polyak_model.goals] = goal_obs
if states is not None:
td_map[self.train_model.S] = states
td_map[self.train_model.M] = masks
td_map[self.polyak_model.S] = states
td_map[self.polyak_model.M] = masks
if verbose:
names_ops_policy = self.names_ops_policy.copy()
values_ops_policy = self.sess.run(self.run_ops_policy,
td_map)[1:] # strip off _train
else:
names_ops_policy = self.names_ops_policy.copy(
)[:8] # not including trust region
values_ops_policy = self.sess.run(self.run_ops_policy,
td_map)[1:][:8]
unimportant_key = ["loss_f", "loss_bc"]
for name in names_ops_policy.copy():
for suffix in unimportant_key:
if name.endswith(suffix):
index = names_ops_policy.index(name)
names_ops_policy.pop(index)
values_ops_policy.pop(index)
break
return names_ops_policy, values_ops_policy
def train_dynamics(self, obs, actions, next_obs, steps, nb_epoch=1):
value_ops_dynamics = []
for epoch in range(nb_epoch):
cur_lr = self.lr.value_steps(steps)
td_map = {
self.dynamics.obs: obs,
self.dynamics.next_obs: next_obs,
self.dynamics.ac: actions,
self.LR: cur_lr
}
value = self.sess.run(self.run_ops_dynamics, td_map)[1:]
value_ops_dynamics.append(value)
value_ops_dynamics = np.asarray(value_ops_dynamics)
value_ops_dynamics = list(np.mean(value_ops_dynamics, axis=0))
return self.name_ops_dynamics.copy(), value_ops_dynamics
def step(self, observation, **kwargs):
if self.residual and not self.dynamics.dummy:
kwargs["goals"] = kwargs["goals"] - observation
return self.step_model.evaluate(
[self.step_model.action, self.step_model_p, self.step_model.state],
observation, **kwargs)
class MlpPolicy(object):
recurrent = False
def __init__(self, name, *args, **kwargs):
self.scope = name
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
self._init(*args, **kwargs)
def _init(self,
ob_space,
ac_space,
hid_size,
num_hid_layers,
gaussian_fixed_var=False,
popart=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
ob = U.get_placeholder(name="ob",
dtype=tf.float32,
shape=[None] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
with tf.variable_scope("popart"):
self.v_rms = RunningMeanStd(shape=[1])
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0,
5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
dense(last_out,
hid_size,
"vffc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
self.norm_vpred = dense(last_out,
1,
"vffinal",
weight_init=U.normc_initializer(1.0))[:, 0]
if popart:
self.vpred = denormalize(self.norm_vpred, self.v_rms)
else:
self.vpred = self.norm_vpred
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
dense(last_out,
hid_size,
"polfc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = dense(last_out,
pdtype.param_shape()[0] // 2, "polfinal",
U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd",
shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = dense(last_out,
pdtype.param_shape()[0], "polfinal",
U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
# change for BC
stochastic = U.get_placeholder(name="stochastic",
dtype=tf.bool,
shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.mean_and_logstd = U.function([ob], [self.pd.mean, self.pd.logstd])
self.ac = ac
self._act = U.function([stochastic, ob], [ac, self.vpred])
self.use_popart = popart
if popart:
self.init_popart()
ret = tf.placeholder(tf.float32, [None])
vferr = tf.reduce_mean(tf.square(self.vpred - ret))
self.vlossandgrad = U.function([ob, ret],
U.flatgrad(vferr,
self.get_vf_variable()))
def init_popart(self):
old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.v_rms.std
old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.v_rms.mean
renormalize_Q_outputs_op = []
vs = self.output_vars
M, b = vs
renormalize_Q_outputs_op += [M.assign(M * old_std / new_std)]
renormalize_Q_outputs_op += [
b.assign((b * old_std + old_mean - new_mean) / new_std)
]
self.renorm_v = U.function([old_std, old_mean], [],
updates=renormalize_Q_outputs_op)
def act(self, stochastic, ob):
ac1, vpred1 = self._act(stochastic, ob[None])
return ac1[0], vpred1[0]
def get_mu_logstd(self, ob):
mean, logstd = self.mean_and_logstd(ob[None])
return mean[0], logstd[0]
def get_variables(self):
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope)
def get_trainable_variables(self):
return tf.trainable_variables(self.scope)
def get_initial_state(self):
return []
def get_vf_variable(self):
return tf.trainable_variables(self.scope + "/vf")
def update_popart(self, v_targets):
old_mean, old_std = U.get_session().run(
[self.v_rms.mean, self.v_rms.std])
self.v_rms.update(v_targets)
self.renorm_v(old_std, old_mean)
@property
def output_vars(self):
output_vars = [
var for var in self.get_vf_variable() if 'vffinal' in var.name
]
return output_vars
def save_policy(self, name):
U.save_variables(name, variables=self.get_variables())
def load_policy(self, name):
U.load_variables(name, variables=self.get_variables())
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
concat_z = np.zeros((observation_shape[0] - 2))
z = np.zeros((119))
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.encoding = tf.placeholder(tf.float32,
shape=(None, ) + z.shape,
name='encoding')
self.kls = tf.placeholder(tf.float32, shape=(None, 1), name='kls')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
self.normalized_obs0 = tf.clip_by_value(
normalize(self.obs0, self.obs_rms), self.observation_range[0],
self.observation_range[1])
self.normalized_obs1 = tf.clip_by_value(
normalize(self.obs1, self.obs_rms), self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
# Create target networks.
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
# Main Actor Network
self.actor_tf = actor(self.normalized_obs0)
# Main Actor network based kl for initial state
self.encoder_tf_mu, self.encoder_tf_sigma = actor(
self.normalized_obs0, True)
# Target Actor network based kl for initial state
self.t_encoder_tf_mu, self.t_encoder_tf_sigma = target_actor(
self.normalized_obs0, True)
# Main Critic Network
self.normalized_critic_tf = critic(self.normalized_obs0,
self.encoder_tf_mu, self.actions)
self.normalized_critic_with_actor_tf = critic(self.normalized_obs0,
self.encoder_tf_mu,
self.actor_tf,
reuse=True)
# only for stats
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
# Main network based kl of initial state for actor optimization
self.kl = tf.reduce_sum(tf.exp(self.encoder_tf_sigma) +
tf.square(self.encoder_tf_mu) - 1. -
self.encoder_tf_sigma,
axis=1)
# Target network based kl of initial state for actor optimization
self.t0_kl = tf.reshape(
tf.reduce_sum(tf.exp(self.t_encoder_tf_sigma) +
tf.square(self.t_encoder_tf_mu) - 1. -
self.t_encoder_tf_sigma,
axis=1), (-1, 1))
Q_obs1 = denormalize(
target_critic(self.normalized_obs1, self.t_encoder_tf_mu,
self.target_actor(self.normalized_obs1)),
self.ret_rms)
self.target_Q = self.rewards + 0.0 * self.kls + (
1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(self.normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.initial_state = None # recurrent architectures not supported yet
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm) \
- 0.0 * U.flatgrad(self.kl, self.actor.trainable_vars)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if var.name.endswith('/w:0') and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def step(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
# find z here as first thing
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action[0].shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def learnt_step(self, obs):
actor_tf = self.actor_tf
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
print('running sanity check')
target_Q_new, new_mean, new_std = self.sess.run(
[self.target_Q, self.ret_rms.mean, self.ret_rms.std],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
print(target_Q_new, target_Q, new_mean, new_std)
assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
t0_kl = self.sess.run(self.t0_kl,
feed_dict={
self.obs0: batch['obs0'],
})
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.obs0:
batch['obs0'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
self.kls:
t0_kl,
})
# Get all gradients and perform a synced update.
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
[
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
],
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
try:
from mpi4py import MPI
except ImportError:
MPI = None
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
def save(self, save_path):
U.save_variables(save_path, None, self.sess)
def load(self, sess, save_path):
self.sess = sess
U.load_variables(save_path, None, self.sess)
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
B = obs0.shape[0]
for b in range(B):
self.memory.append(obs0[b], action[b], reward[b], obs1[b],
terminal1[b])
if self.normalize_observations:
self.obs_rms.update(np.array([obs0[b]]))
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
class DDPG_paramnoise(object):
"""
Implicit Policy Optimization for DDPG
noise injected in the middle of blackbox (param noise)
"""
def __init__(self,
maxactor,
maxentactor,
critic,
classifier,
memory,
fifomemory,
observation_shape,
action_shape,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
critic_l2_reg=0.,
classifier_l2_reg=0.,
maxactor_lr=1e-4,
maxentactor_lr=1e-4,
critic_lr=1e-3,
classifier_lr=1e-3,
clip_norm=None,
reward_scale=1.,
entropy_coeff=1.,
beta=0.0):
# Inputs.
self.obs0_act = tf.placeholder(tf.float32,
shape=(1, ) + observation_shape,
name='obs0_act')
self.obs0_train = tf.placeholder(tf.float32,
shape=(batch_size, ) +
observation_shape,
name='obs0_train')
self.obs1 = tf.placeholder(tf.float32,
shape=(batch_size, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions_act = tf.placeholder(tf.float32,
shape=(1, ) + action_shape,
name='actions_act')
self.actions_train = tf.placeholder(tf.float32,
shape=(64, ) + action_shape,
name='actions_train')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.fifomemory = fifomemory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.action_shape = action_shape
self.critic = critic
self.maxactor = maxactor
self.maxentactor = maxentactor
self.classifier = classifier
self.maxactor_lr = maxactor_lr
self.maxentactor_lr = maxentactor_lr
self.critic_lr = critic_lr
self.classifier_lr = classifier_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.classifier_l2_reg = classifier_l2_reg
self.entropy_coeff = entropy_coeff
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0_act = tf.clip_by_value(
normalize(self.obs0_act, self.obs_rms), self.observation_range[0],
self.observation_range[1])
normalized_obs0_train = tf.clip_by_value(
normalize(self.obs0_train, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
self.normalized_obs0_act = normalized_obs0_act # record normalized_obs0
self.normalized_obs0_train = normalized_obs0_train
self.normalized_obs1 = normalized_obs1
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_maxactor = copy(maxactor)
target_maxentactor = copy(maxentactor)
target_maxactor.name = 'target_maxactor'
self.target_maxactor = target_maxactor
target_maxentactor.name = 'target_maxentactor'
self.target_maxentactor = target_maxentactor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.maxactor_tf_act = maxactor(normalized_obs0_act)
self.maxentactor_tf_act = maxentactor(normalized_obs0_act)
self.maxactor_tf_train = maxactor(normalized_obs0_train, reuse=True)
self.maxentactor_tf_train = maxentactor(normalized_obs0_train,
reuse=True)
nb_actions = maxactor.nb_actions
# Create interpolated action for act
batch_act = self.maxactor_tf_act.get_shape().as_list()[0]
mask_act = tf.random_uniform(
tf.stack([batch_act]), minval=0, maxval=1, dtype=tf.float32) < beta
self.actor_tf_act = tf.where(mask_act, self.maxactor_tf_act,
self.maxentactor_tf_act)
# Create interpolated action for train
batch_train = self.maxactor_tf_train.get_shape().as_list()[0]
mask_train = tf.random_uniform(
tf.stack([batch_train
]), minval=0, maxval=1, dtype=tf.float32) < beta
self.actor_tf_train = tf.where(mask_train, self.maxactor_tf_train,
self.maxentactor_tf_train)
# Create graphs for critic for train
self.normalized_critic_tf = critic(normalized_obs0_train,
self.actions_train)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_maxactor_tf = critic(
normalized_obs0_train, self.maxactor_tf_train, reuse=True)
self.normalized_critic_with_maxentactor_tf = critic(
normalized_obs0_train, self.maxentactor_tf_train, reuse=True)
self.normalized_critic_with_actor_tf = critic(normalized_obs0_act,
self.actor_tf_act,
reuse=True) # act
self.critic_with_maxactor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_maxactor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
self.critic_with_maxentactor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_maxentactor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
# Create interpolated target action for train
batch_train = normalized_obs0_train.get_shape().as_list()[0]
mask_train = tf.random_uniform(
tf.stack([batch_train
]), minval=0, maxval=1, dtype=tf.float32) < beta
self.target_actions = tf.where(
mask_train, self.target_maxactor(normalized_obs1),
self.target_maxentactor(normalized_obs1))
Q_obs1 = denormalize(
target_critic(normalized_obs1, self.target_actions), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Create graphs for critic for act
#self.normalized_critic_tf_act = critic(normalized_obs0_act, self.actions_act)
#self.critic_tf_act = denormalize(tf.clip_by_value(self.normalized_critic_tf_act, self.return_range[0], self.return_range[1]), self.ret_rms)
# Classifier Network
self.random_actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='random_actions')
#self.logit = classifier(normalized_obs0_train, self.actor_tf_train) # actions produced by policy for backprop
self.logit = classifier(normalized_obs0_train,
self.maxentactor_tf_train)
self.random_logit = classifier(normalized_obs0_train,
self.random_actions,
reuse=True)
# Set up parts.
self.setup_approx_entropy()
self.setup_actor_optimizer()
self.setup_critic_optimizer()
self.setup_classifier_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.maxactor.vars, self.target_maxactor.vars, self.tau)
actor_init_updates_, actor_soft_updates_ = get_target_updates(
self.maxentactor.vars, self.target_maxentactor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [
actor_init_updates, actor_init_updates_, critic_init_updates
]
self.target_soft_updates = [
actor_soft_updates, actor_soft_updates_, critic_soft_updates
]
def setup_approx_entropy(self):
logger.info('setting up approx entropy')
self.approx_entropy = -tf.reduce_mean(self.logit)
def setup_actor_optimizer(self):
# maxactor
logger.info('setting up maxactor optimizer')
self.maxactor_loss = -tf.reduce_mean(self.critic_with_maxactor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.maxactor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
# Add entropy into actor loss
self.maxactor_grads = U.flatgrad(self.maxactor_loss,
self.maxactor.trainable_vars,
clip_norm=self.clip_norm)
self.maxactor_optimizer = MpiAdam(
var_list=self.maxactor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
# maxentactor
logger.info('setting up maxentactor optimizer')
self.maxentactor_loss = -tf.reduce_mean(
self.critic_with_maxentactor_tf)
actor_shapes = [
var.get_shape().as_list()
for var in self.maxentactor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
logger.info('using entropy coeff {}'.format(self.entropy_coeff))
self.maxentactor_loss += -self.entropy_coeff * self.approx_entropy
# Add entropy into actor loss
self.maxentactor_grads = U.flatgrad(self.maxentactor_loss,
self.maxentactor.trainable_vars,
clip_norm=self.clip_norm)
self.maxentactor_optimizer = MpiAdam(
var_list=self.maxentactor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_classifier_optimizer(self):
logger.info('setting up classifier optimizer')
#self.classifier_loss = - (tf.reduce_mean(tf.log(1e-8 + tf.sigmoid(self.logit)))
# + tf.reduce_mean(tf.log(1e-8 + 1 - tf.sigmoid(self.random_logit))))
label_zeros = tf.zeros_like(self.logit)
label_ones = tf.ones_like(self.random_logit)
self.classifier_loss = (tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=self.logit, labels=label_zeros)) + tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=self.random_logit, labels=label_ones)))
if self.classifier_l2_reg > 0.:
classifier_reg_vars = [
var for var in self.classifier.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in classifier_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.classifier_l2_reg))
classifier_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.classifier_l2_reg),
weights_list=classifier_reg_vars)
self.classifier_loss += classifier_reg
classifier_shapes = [
var.get_shape().as_list() for var in self.classifier.trainable_vars
]
classifier_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in classifier_shapes])
logger.info(' classifier shapes: {}'.format(classifier_shapes))
logger.info(' classifier params: {}'.format(classifier_nb_params))
self.classifier_grads = U.flatgrad(self.classifier_loss,
self.classifier.trainable_vars,
clip_norm=self.clip_norm)
self.classifier_optimizer = MpiAdam(
var_list=self.classifier.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
#ops += [tf.reduce_mean(self.critic_with_actor_tf)]
#names += ['reference_actor_Q_mean']
#ops += [reduce_std(self.critic_with_actor_tf)]
#names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf_train)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf_train)]
names += ['reference_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if apply_noise:
actor_tf = self.actor_tf_act # TODO: handle apply_noise=False mode
else:
actor_tf = self.actor_tf_act # should take the mean?? probably not
feed_dict = {self.obs0_act: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
self.fifomemory.append(obs0, action)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get a batch from recent policy then update classifier
batch_recent = self.fifomemory.sample(batch_size=self.batch_size)
random_actions = np.random.uniform(
low=self.action_range[0],
high=self.action_range[1],
size=[self.batch_size,
np.prod(np.array(self.action_shape))]).astype('float32')
ops = [
self.classifier_grads, self.classifier_loss, self.approx_entropy
]
classifier_grads, classifier_loss, approx_entropy = self.sess.run(
ops,
feed_dict={
self.obs0_train: batch_recent['obs0'],
self.random_actions: random_actions
})
self.classifier_optimizer.update(classifier_grads,
stepsize=self.classifier_lr)
# Get all gradients and perform a synced update.
ops = [
self.maxactor_grads, self.maxactor_loss, self.maxentactor_grads,
self.maxentactor_loss, self.critic_grads, self.critic_loss
]
maxactor_grads, maxactor_loss, maxentactor_grads, maxentactor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0_train: batch['obs0'],
self.actions_train: batch['actions'],
self.critic_target: target_Q,
})
self.maxactor_optimizer.update(maxactor_grads,
stepsize=self.maxactor_lr)
self.maxentactor_optimizer.update(maxentactor_grads,
stepsize=self.maxentactor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, maxactor_loss, maxentactor_loss, classifier_loss, approx_entropy
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.maxactor_optimizer.sync()
self.maxentactor_optimizer.sync()
self.critic_optimizer.sync()
self.classifier_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
#print(self.stats_sample['obs0'].shape, self.stats_sample['actions'].shape)
#print(self.obs0_train, self.actions_train)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0_train:
self.stats_sample['obs0'],
self.actions_train:
self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
return stats
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
class BDDPG(object):
def __init__(self,
actor,
critic,
obs_dim,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.95,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.expert_qv = tf.placeholder(tf.float32,
shape=(None, 1),
name='expert_qv')
self.expert_qv1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='expert_qv1')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.expert_actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='expert_actions')
self.expert_actions1 = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='expert_actions1')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = copy(actor)
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.obs_dim = obs_dim
# self.critic_obs0 = self.experts[0].obs0
# self.critic_obs1 = self.experts[0].obs1
# self.critic_actor = self.experts[0].use_tf_actor
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(self.actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
bel0 = self.obs0[:, obs_dim:]
bel_dim = observation_shape[0] - obs_dim
entropy = -tf.reduce_sum(
bel0 * tf.log(bel0 + 1e-3) / math.log(bel_dim), axis=1) / bel_dim
# entropy = tf.Print(entropy, [entropy], '>>>> entropy :', summarize=10)
entropy = tf.expand_dims(0.1 * entropy, 1)
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = entropy * self.actor(
normalized_obs0,
self.expert_qv) + (1 - entropy) * self.expert_actions
self.normalized_critic_tf = critic(normalized_obs0, self.actions,
self.expert_qv)
self.critic_tf = tf.clip_by_value(self.normalized_critic_tf,
self.return_range[0],
self.return_range[1])
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
self.expert_qv)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
bel1 = self.obs1[:, obs_dim:]
entropy1 = -tf.reduce_sum(
bel1 * tf.log(bel1 + 1e-3) / math.log(bel_dim), axis=1) / bel_dim
entropy1 = tf.expand_dims(0.1 * entropy1, 1)
action1 = entropy1 * target_actor(normalized_obs1, self.expert_qv1) + (
1 - entropy1) * self.expert_actions1
self.Q_obs1 = target_critic(normalized_obs1, action1, self.expert_qv1)
# self.Q_obs1 = tf.Print(self.Q_obs1, [self.Q_obs1], '>>>> Q :', summarize=10)
# self.terminals1 = tf.Print(self.terminals1, [self.terminals1], '>>>> terminal :', summarize=10)
self.target_Q = self.rewards + (1. -
self.terminals1) * gamma * self.Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0, self.expert_qv)
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.initial_state = None # recurrent architectures not supported yet
def setup_target_network_updates(self):
# import IPython; IPython.embed() ; import sys; sys.exit(0)
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.perturbable_vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0, expert_qv0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0,
expert_qv0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0,
expert_qv0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if var.name.endswith('/w:0') and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def step(self,
obs,
expert_qv,
expert_action,
apply_noise=True,
compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {
self.obs0:
U.adjust_shape(self.obs0, [obs]),
self.expert_qv:
U.adjust_shape(self.expert_qv, [expert_qv]),
self.expert_actions:
U.adjust_shape(self.expert_actions, [expert_action])
}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action[0].shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def store_transition(self, obs0, expert_qv, action, expert_action, reward,
obs1, expert_qv1, expert_action1, terminal1):
reward *= self.reward_scale
# B = obs0.shape[0]
# for b in range(B):
# self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b])
# if self.normalize_observations:
# self.obs_rms.update(np.array([obs0[b]]))
self.memory.append(obs0, expert_qv, action, expert_action, reward,
obs1, expert_qv1, expert_action1, terminal1)
if self.normalize_observations:
self.obs_rms.update(obs0)
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
# import IPython; IPython.embed(); import sys; sys.exit(0)
target_Q, Q_obs1 = self.sess.run(
[self.target_Q, self.Q_obs1],
feed_dict={
self.obs1: batch['obs1'],
self.expert_qv1: batch['expert_qv1'],
self.expert_actions1: batch['expert_actions1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.expert_qv: batch['expert_qv'],
self.actions: batch['actions'],
self.expert_actions: batch['expert_actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
# import IPython; IPython.embed(); exit(0)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
# self.graph = graph
self.sess.run(tf.global_variables_initializer())
self.setup_actor_optimizer()
self.setup_critic_optimizer()
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0:
self.stats_sample['obs0'],
self.actions:
self.stats_sample['actions'],
self.expert_qv:
self.stats_sample['expert_qv'],
self.expert_actions:
self.stats_sample['expert_actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
try:
from mpi4py import MPI
except ImportError:
MPI = None
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.expert_actions:
batch['expert_actions'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
def save(self, path):
save_variables(path)
def load(self, path):
load_variables(path)
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
critic_l2_reg=0.,
actor_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.,
bc_teacher_lambda=0.0,
use_qfilter=False):
"""DDPG.
I changed observation_range to (0, 255) for the image-based RL part
because we don't divide our images by 255 until later. The action and
return range should be OK.
"""
# Inputs. Daniel: for images, cast to a new variable which gets cast to the float.
# Assumes we detect via observation space; I think MuJoCo envs have obs shape length 1.
# Then we let the remainder be input to subsequent code that uses observations.
if len(observation_shape) > 1:
self.obs0 = tf.placeholder(tf.int32,
shape=(None, ) + observation_shape,
name='obs0_imgs')
self.obs1 = tf.placeholder(tf.int32,
shape=(None, ) + observation_shape,
name='obs1_imgs')
self.obs0_f_imgs = tf.cast(self.obs0, tf.float32) / 255.0
self.obs1_f_imgs = tf.cast(self.obs1, tf.float32) / 255.0
assert not normalize_observations, 'Why normalize if we already divide by 255?'
observation_range = (-np.inf, np.inf
) # We don't want to clip raw pixels here.
self.use_images = True
self.bc_teacher_lambda = bc_teacher_lambda
self.use_qfilter = use_qfilter
else:
# Assuming default MuJoCo settings here.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.use_images = False
self.bc_teacher_lambda = bc_teacher_lambda
self.actor_l2_reg = 0.0
self.use_qfilter = use_qfilter
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Daniel: new for demos.
self.flag_teacher = tf.placeholder(tf.float32,
shape=(None, 1),
name='flag_teacher')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.actor_l2_reg = actor_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
# Daniel: this is where all the obs are subsequently passed, thus handle image case.
# That way our feed_dicts in later code can still use self.{obs0,obs1}.
if self.use_images:
normalized_obs0 = tf.clip_by_value(
normalize(self.obs0_f_imgs, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(
normalize(self.obs1_f_imgs, self.obs_rms),
self.observation_range[0], self.observation_range[1])
else:
normalized_obs0 = tf.clip_by_value(
normalize(self.obs0, self.obs_rms), self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(
normalize(self.obs1, self.obs_rms), self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
# One actor. Two critics: action can be from:
# (1) itself (supplied via placeholder) -- for critic update, Q(s,a) sampled from RBuffer.
# (2) from actor_tf, supplied by the actor -- for actor update which maximizes Q(s,pi(o)).
# Then create two de-normalized versions of those critics.
# self.critic_tf : Q(s,a) where a is supplied by placeholder
# self.critic_with_actor_tf : Q(s,pi(s)) where pi(s) is the actor
# Finally, get target Q values from target critic/actor.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Daniel: add a Q-filter, 1 if Q(s,a) > Q(s,pi(s)) where former has `a` from *demonstrator*. Only after pre-training?
self.flag_qfilter = tf.cast(self.critic_tf > self.critic_with_actor_tf,
tf.float32)
self.during_pretrain = tf.placeholder(tf.float32, (),
name="during_pretrain_flag")
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.initial_state = None # recurrent architectures not supported yet
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
"""Make actor loss, grads, and optimizer. Several changes:
We use a behavior cloning loss (with a Q-filter on top of that), using
actor_tf for the current actor's output given the state, and actions as
placeholder for what was sampled from the buffer. The latter might have
student actions, in which case we ignore these w/the flag.
We apply L2 reg if desired (following DeepMind's DDPGfD). Careful
w/variable names if we switch network construction code!!
(Nair et al., 2018) set the `bc_teacher_lambda` term I'm using to 1,
and average out the BC loss by all items in the batch, *regardless* of
whether the item passed the Q-filter or not. We're doing the same here
by dividing by the sum of the number of teacher flags.
"""
logger.info('\nsetting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
if self.bc_teacher_lambda > 0.:
# Daniel: add Behavior cloning loss to the actor, but only on teacher samples!
# I'm doing a reduce_sum and dividing by the total in the flag teacher.
self._diff_m = self.actor_tf - self.actions
self._diff_v = tf.reduce_mean(tf.square(self._diff_m),
axis=1,
keepdims=True)
self._diff_f = self._diff_v * self.flag_teacher
# Daniel: another idea is to apply q-filters only if we are past pre-training.
if self.use_qfilter:
logger.info(' applying Q-filter flag: {}'.format(
self.flag_qfilter))
self._diff_f = tf.cond(
self.during_pretrain > 0.5,
lambda: self._diff_f, # pretrain? identity
lambda: self._diff_f * self.flag_qfilter
) # else? apply filter
self.bc_loss = tf.reduce_sum(
self._diff_f) / (tf.reduce_sum(self.flag_teacher) + 1e-6)
self.actor_loss += self.bc_loss
logger.info(' applying BC loss to actor with {}'.format(
self.bc_teacher_lambda))
logger.info(' diff_matrix: {}'.format(self._diff_m))
logger.info(' diff_vector: {}'.format(self._diff_v))
logger.info(' diff_filter: {}'.format(self._diff_f))
if self.actor_l2_reg > 0.:
actor_reg_vars = [
var for var in self.actor.trainable_vars
if ((var.name.endswith('/w:0') or var.name.endswith(
'/kernel:0')) and 'output' not in var.name)
]
for var in actor_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.actor_l2_reg))
actor_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.actor_l2_reg),
weights_list=actor_reg_vars)
self.actor_loss += actor_reg
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}\n'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
"""Make critic loss, grads, and optimizer. Minor change w/L2 regularization.
I didn't realize that our custom code would name the variables a bit different.
It actually makes a huge difference, as the critic's default L2 is 0.01. Just be
careful if we decide to re-name the variables or use a different TF construction.
"""
logger.info('\nsetting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if ((var.name.endswith('/w:0') or var.name.endswith(
'/kernel:0')) and 'output' not in var.name)
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}\n'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def step(self, obs, apply_noise=True, compute_Q=True):
"""Apply the policy.
Note the noise: for DDPG if we are *deploying* it, we should probably
set the noise to False, such as for the `--play` option.
"""
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
#assert noise.shape == action[0].shape # daniel: with my fix, both are (numenv, acdim)
assert noise.shape == action.shape, '{} {}'.format(
noise.shape, action.shape)
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None
def store_transition(self,
obs0,
action,
reward,
obs1,
terminal1,
is_teacher=False):
"""Store transitions for DDPG.
Daniel: collected via VecEnv, so iterate through batch size and append
individual components. It's serial but shouldn't be a time bottleneck.
Note that all this seems to be done using one-step returns; I don't see
n-step returns anywhere. Also, we should add an indication if this is a
teacher sample.
"""
reward *= self.reward_scale
B = obs0.shape[0]
for b in range(B):
self.memory.append(obs0[b],
action[b],
reward[b],
obs1[b],
terminal1[b],
is_teacher=is_teacher)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0[b]]))
def train(self, during_pretrain=False):
"""Daniel: added during_pretrain in case we want to do anything different there.
By default it's false (and float(during_pretrain)=0.0) to maintain backwards compatibility.
"""
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = \
self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
## Daniel: use this for debugging extra DDPG features we implemented:
#ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss,
# self.critic_tf, self.critic_with_actor_tf, self.flag_teacher,
# self.flag_qfilter, self._diff_f, self.actor_tf, self.actions]
#actor_grads, actor_loss, critic_grads, critic_loss, Q_demo, Q_actor, flag_t, flag_q, diff_f, act_tf, act_ph = \
# self.sess.run(ops, feed_dict={
# self.obs0: batch['obs0'],
# self.actions: batch['actions'],
# self.critic_target: target_Q,
# self.flag_teacher: batch['flag_teacher'],
# self.during_pretrain: float(during_pretrain),
#})
#print('\nQ(s,a), Q(s,pi(s)), act_tf, act_ph, diff_f, flag_q, flag_t')
#print(Q_demo.T)
#print(Q_actor.T)
#print('now actors:')
#print(act_tf.T)
#print(act_ph.T)
#print('now diff/flags:')
#print(diff_f.T)
#print(flag_q.T)
#print(flag_t.T)
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
self.flag_teacher: batch['flag_teacher'],
self.during_pretrain: float(during_pretrain),
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
# Daniel: following PPO2 code outline, hoping to save/load models.
self.save = functools.partial(save_variables, sess=sess)
self.load = functools.partial(load_variables, sess=sess)
# Daniel: back to normal.
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
try:
from mpi4py import MPI
except ImportError:
MPI = None
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
if MPI is not None:
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
else:
mean_distance = distance
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
def learn(
*,
network,
env,
total_timesteps,
timesteps_per_batch=1024, # what to train on
max_kl=0.001,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
load_path=None,
novelty_reward='AE',
normalize_int_rew=False,
**network_kwargs):
'''
learn a policy function with TRPO algorithm
Parameters:
----------
network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
or function that takes input placeholder and returns tuple (output, None) for feedforward nets
or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
timesteps_per_batch timesteps per gradient estimation batch
max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) )
ent_coef coefficient of policy entropy term in the optimization objective
cg_iters number of iterations of conjugate gradient algorithm
cg_damping conjugate gradient damping
vf_stepsize learning rate for adam optimizer used to optimie value function loss
vf_iters number of iterations of value function optimization iterations per each policy optimization step
total_timesteps max number of timesteps
max_episodes max number of episodes
max_iters maximum number of policy optimization iterations
callback function to be called with (locals(), globals()) each policy optimization step
load_path str, path to load the model from (default: None, i.e. no model is loaded)
**network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
Returns:
-------
learnt model
'''
if MPI is not None:
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
else:
nworkers = 1
rank = 0
cpus_per_worker = 1
U.get_session(
config=tf.ConfigProto(allow_soft_placement=True,
inter_op_parallelism_threads=cpus_per_worker,
intra_op_parallelism_threads=cpus_per_worker))
policy = build_policy(env, network, value_network='copy', **network_kwargs)
set_global_seeds(seed)
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
ob = observation_placeholder(ob_space)
with tf.variable_scope("pi"):
pi = policy(observ_placeholder=ob)
with tf.variable_scope("oldpi"):
oldpi = policy(observ_placeholder=ob)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = ent_coef * meanent
vferr = tf.reduce_mean(tf.square(pi.vf - ret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = get_trainable_variables("pi")
# var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
# vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
var_list = get_pi_trainable_variables("pi")
vf_var_list = get_vf_trainable_variables("pi")
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(get_variables("oldpi"), get_variables("pi"))
])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
rff_rms_int = RunningMeanStd()
nr = NOVELTY_REWARDS[novelty_reward](env.observation_space)
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
if MPI is not None:
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
else:
out = np.copy(x)
return out
U.initialize()
if load_path is not None:
pi.load(load_path)
th_init = get_flat()
if MPI is not None:
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
if sum([max_iters > 0, total_timesteps > 0, max_episodes > 0]) == 0:
# noththing to be done
return pi
assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \
'out of max_iters, total_timesteps, and max_episodes only one should be specified'
while True:
if callback: callback(locals(), globals())
if total_timesteps and timesteps_so_far >= total_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************" % iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
# Calculate novelty rewards
bonus = nr.get_batch_bonus_and_update(seg["ob"])
if normalize_int_rew:
rff_rms_int.update(bonus.ravel())
bonus = bonus / rff_rms_int.std.eval()
seg["orig_rew"] = seg["rew"]
seg["rew"] = seg["rew"] + bonus
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
if MPI is not None:
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
else:
listoflrpairs = [lrlocal]
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
return pi
class RND(object):
def __init__(self, name, ph_ob, args):
self.convfeat = args.convfeat
self.rep_size = args.rep_size
self.enlargement = args.enlargement
self.proportion_of_exp_used_for_predictor_update = args.proportion_of_exp_used_for_predictor_update
self.scope = name
with tf.variable_scope(self.scope):
self.build_graph = self.build_graph(ph_ob)
def build_graph(self, ph_ob):
ob = ph_ob[-1]
assert len(ob.shape.as_list()) == 4 #B, H, W, C
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob.shape.as_list()[1:3] + [1])
ob_norm = ob[:, :, :, -1:]
ob_norm = tf.cast(ob_norm, tf.float32)
ob_norm = tf.clip_by_value(
(ob_norm - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
# Random target network
xr = tf.nn.leaky_relu(
conv(ob_norm,
"c1r",
nf=self.convfeat * 1,
rf=8,
stride=4,
init_scale=np.sqrt(2)))
xr = tf.nn.leaky_relu(
conv(xr,
'c2r',
nf=self.convfeat * 2 * 1,
rf=4,
stride=2,
init_scale=np.sqrt(2)))
xr = tf.nn.leaky_relu(
conv(xr,
'c3r',
nf=self.convfeat * 2 * 1,
rf=3,
stride=1,
init_scale=np.sqrt(2)))
rgbr = [to2d(xr)]
X_r = fc(rgbr[0], 'fc1r', nh=self.rep_size, init_scale=np.sqrt(2))
# Predictor network
xrp = tf.nn.leaky_relu(
conv(ob_norm,
'c1rp_pred',
nf=self.convfeat,
rf=8,
stride=4,
init_scale=np.sqrt(2)))
xrp = tf.nn.leaky_relu(
conv(xrp,
'c2rp_pred',
nf=self.convfeat * 2,
rf=4,
stride=2,
init_scale=np.sqrt(2)))
xrp = tf.nn.leaky_relu(
conv(xrp,
'c3rp_pred',
nf=self.convfeat * 2,
rf=3,
stride=1,
init_scale=np.sqrt(2)))
rgbrp = to2d(xrp)
X_r_hat = tf.nn.relu(
fc(rgbrp,
'fc1r_hat1_pred',
nh=256 * self.enlargement,
init_scale=np.sqrt(2)))
X_r_hat = tf.nn.relu(
fc(X_r_hat,
'fc1r_hat2_pred',
nh=256 * self.enlargement,
init_scale=np.sqrt(2)))
X_r_hat = fc(X_r_hat,
'fc1r_hat3_pred',
nh=self.rep_size,
init_scale=np.sqrt(2))
self.feat_var = tf.reduce_mean(tf.nn.moments(X_r, axes=[0])[1])
self.max_feat = tf.reduce_max(tf.abs(X_r))
self.int_rew = tf.reduce_mean(
tf.square(tf.stop_gradient(X_r) - X_r_hat),
axis=-1,
keep_dims=True)
targets = tf.stop_gradient(X_r)
# self.aux_loss = tf.reduce_mean(tf.square(noisy_targets-X_r_hat))
self.aux_loss = tf.reduce_mean(tf.square(targets - X_r_hat), -1)
mask = tf.random_uniform(shape=tf.shape(self.aux_loss),
minval=0.,
maxval=1.,
dtype=tf.float32)
mask = tf.cast(mask < self.proportion_of_exp_used_for_predictor_update,
tf.float32)
self.aux_loss = tf.reduce_sum(mask * self.aux_loss) / tf.maximum(
tf.reduce_sum(mask), 1.)
self._predictor = U.function([ob], [self.int_rew])
def predict(self, ob):
obf = ob[-1]
if obf.shape == 3:
obf = np.expand_dims(obf, 0)
int_rew = self._predictor(obf)[0]
return int_rew
def update_obs_rms(self, ob):
obf = np.array(zip(*ob.tolist())[1])
self.ob_rms.update(obf)
def get_variables(self):
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope)
def get_trainable_variables(self):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
state_shape,
aux_shape,
lambda_obj_conf_predict,
lambda_gripper_predict,
lambda_target_predict,
action_noise=None,
gamma=0.99,
tau=0.001,
enable_popart=False,
normalize_observations=True,
normalize_state=True,
normalize_aux=True,
batch_size=128,
observation_range=(-10., 10.),
action_range=(-1., 1.),
state_range=(-4, 4),
return_range=(-250, 10),
aux_range=(-10, 10),
critic_l2_reg=0.001,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.,
replay_beta=0.4,
lambda_1step=1.0,
lambda_nstep=1.0,
nsteps=10,
run_name="unnamed_run",
lambda_pretrain=0.0,
target_policy_noise=0.2,
target_policy_noise_clip=0.5,
policy_and_target_update_period=2,
num_critics=2,
**kwargs):
# Inputs.
self.obs0 = tf.placeholder(
tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs1 = tf.placeholder(
tf.float32, shape=(None,) + observation_shape, name='obs1')
self.state0 = tf.placeholder(
tf.float32, shape=(None,) + state_shape, name='state0')
self.state1 = tf.placeholder(
tf.float32, shape=(None,) + state_shape, name='state1')
self.terminals1 = tf.placeholder(
tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(
tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(
tf.float32, shape=(None,) + action_shape, name='actions')
self.critic_target = tf.placeholder(
tf.float32, shape=(None, 1), name='critic_target')
self.nstep_steps = tf.placeholder(
tf.float32, shape=(None, 1), name='nstep_reached')
self.nstep_critic_target = tf.placeholder(
tf.float32, shape=(None, 1), name='nstep_critic_target')
# Memory debug variables - memory and resident set size. Used
# for tensorboard plotting.
self.memory_size = tf.placeholder(
tf.float32, shape=None, name='memory_size')
self.rss = tf.placeholder(tf.float32, shape=None, name='rss')
self.aux0 = tf.placeholder(
tf.float32, shape=(None,) + aux_shape, name='aux0')
self.aux1 = tf.placeholder(
tf.float32, shape=(None,) + aux_shape, name='aux1')
self.pretraining_tf = tf.placeholder(
tf.float32, shape=(None, 1),
name='pretraining_tf')
self.aux_shape = aux_shape
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_state = normalize_state
self.normalize_aux = normalize_aux
self.action_noise = action_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.actor = actor
self.actor_lr = actor_lr
self.state_range = state_range
self.aux_range = aux_range
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.lambda_nstep = lambda_nstep
self.lambda_1step = lambda_1step
self.lambda_obj_conf_predict = lambda_obj_conf_predict
self.lambda_gripper_predict = lambda_gripper_predict
self.lambda_target_predict = lambda_target_predict
self.nsteps = nsteps
self.replay_beta = replay_beta
self.run_name = run_name
self.lambda_pretrain = lambda_pretrain
self.target_policy_noise = target_policy_noise
self.target_policy_noise_clip = target_policy_noise_clip
self.ep = 0
self.policy_and_target_update_period = policy_and_target_update_period
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
if self.normalize_state:
with tf.variable_scope('state_rms'):
self.state_rms = RunningMeanStd(shape=state_shape)
else:
self.state_rms = None
if self.normalize_aux:
with tf.variable_scope('normalize_aux'):
self.aux_rms = RunningMeanStd(shape=aux_shape)
else:
self.aux_rms = None
with tf.name_scope('obs_preprocess'):
self.normalized_obs0 = tf.clip_by_value(
normalize(self.obs0, self.obs_rms), self.observation_range[0],
self.observation_range[1])
self.normalized_obs1 = tf.clip_by_value(
normalize(self.obs1, self.obs_rms), self.observation_range[0],
self.observation_range[1])
with tf.name_scope('state_preprocess'):
self.normalized_state0 = tf.clip_by_value(
normalize(self.state0, self.state_rms), self.state_range[0],
self.state_range[1])
self.normalized_state1 = tf.clip_by_value(
normalize(self.state1, self.state_rms), self.state_range[0],
self.state_range[1])
with tf.name_scope('aux_preprocess'):
self.normalized_aux0 = tf.clip_by_value(
normalize(self.aux0, self.aux_rms), self.aux_range[0],
self.aux_range[1])
self.normalized_aux1 = tf.clip_by_value(
normalize(self.aux1, self.aux_rms), self.aux_range[0],
self.aux_range[1])
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
self.actor_tf, self.obj_conf, self.gripper, self.target = actor(
self.normalized_obs0, self.normalized_aux0)
next_actions, _, _, _ = target_actor(self.normalized_obs1,
self.normalized_aux1)
noise = tf.distributions.Normal(
tf.zeros_like(next_actions), self.target_policy_noise).sample()
noise = tf.clip_by_value(
noise,
-self.target_policy_noise_clip,
self.target_policy_noise_clip,
)
# Initialize single/twin critics.
self.num_critics = num_critics
assert (num_critics == 1 or num_critics == 2)
self.critics = [None] * num_critics
self.target_critics = [None] * num_critics
self.critic_tfs = [None] * num_critics
self.critic_with_actor_tfs = [None] * num_critics
self.step_1_td_losses = [None] * num_critics
self.n_step_td_losses = [None] * num_critics
self.td_errors = [None] * num_critics
self.critic_losses = [None] * num_critics
self.critic_grads = [None] * num_critics
self.critic_optimizers = [None] * num_critics
Q_obs1s = [None] * num_critics
for i in range(num_critics):
current_critic = copy(critic)
current_critic.name = "critic" + str(i)
self.critics[i] = current_critic
self.target_critics[i] = copy(current_critic)
self.target_critics[i].name = 'target_critic' + str(i)
self.critic_tfs[i] = tf.clip_by_value(
current_critic(self.normalized_state0, self.actions,
self.normalized_aux0), self.return_range[0],
self.return_range[1])
self.critic_with_actor_tfs[i] = tf.clip_by_value(
current_critic(
self.normalized_state0,
self.actor_tf,
self.normalized_aux0,
reuse=True), self.return_range[0], self.return_range[1])
Q_obs1s[i] = self.target_critics[i](self.normalized_state1,
next_actions + noise,
self.normalized_aux1)
if num_critics == 2:
minQ = tf.minimum(Q_obs1s[0], Q_obs1s[1])
else:
minQ = Q_obs1s[0]
self.target_Q = self.rewards + \
(1. - self.terminals1) * tf.pow(gamma, self.nstep_steps) * minQ
self.importance_weights = tf.placeholder(
tf.float32, shape=(None, 1), name='importance_weights')
self.setup_actor_optimizer()
self.setup_stats()
self.setup_target_network_updates()
for i in range(num_critics):
self.setup_critic_optimizer(i)
self.setup_summaries()
def setup_target_network_updates(self):
with tf.name_scope('target_net_updates'):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
target_init_updates = [actor_init_updates]
target_soft_updates = [actor_soft_updates]
for i in range(self.num_critics):
init, soft = get_target_updates(self.critics[i].vars,
self.target_critics[i].vars,
self.tau)
target_init_updates.append(init)
target_soft_updates.append(soft)
self.target_init_updates = target_init_updates
self.target_soft_updates = target_soft_updates
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
with tf.name_scope('actor_optimizer'):
self.action_diffs = tf.reduce_mean(
tf.square(self.actions - self.actor_tf), 1)
demo_better_than_actor = self.critic_tfs[
0] > self.critic_with_actor_tfs[0]
demo_better_than_actor = self.pretraining_tf * \
tf.cast(demo_better_than_actor, tf.float32)
self.bc_loss = (
tf.reduce_sum(demo_better_than_actor * self.action_diffs) *
self.lambda_pretrain /
(tf.reduce_sum(self.pretraining_tf) + 1e-6))
self.original_actor_loss = - tf.reduce_mean(self.critic_with_actor_tfs[0])
self.obj_conf_loss = tf.reduce_mean(
tf.square(self.obj_conf -
self.state0[:, 8:11])) * self.lambda_obj_conf_predict
self.gripper_loss = tf.reduce_mean(
tf.square(self.gripper -
self.state0[:, 0:3])) * self.lambda_gripper_predict
self.target_loss = tf.reduce_mean(
tf.square(self.target -
self.state0[:, 3:6])) * self.lambda_target_predict
self.actor_loss = self.original_actor_loss + self.bc_loss + \
self.obj_conf_loss + self.gripper_loss + self.target_loss
self.number_of_demos_better = tf.reduce_sum(
demo_better_than_actor)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(
self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(
var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self, i):
with tf.name_scope('critic_optimizer' + str(i)):
critic_target_tf = tf.clip_by_value(
self.critic_target, self.return_range[0], self.return_range[1])
nstep_critic_target_tf = tf.clip_by_value(self.nstep_critic_target,
self.return_range[0],
self.return_range[1])
td_error = tf.square(self.critic_tfs[i] - critic_target_tf)
self.step_1_td_losses[i] = tf.reduce_mean(
self.importance_weights * td_error) * self.lambda_1step
nstep_td_error = tf.square(self.critic_tfs[i] -
nstep_critic_target_tf)
self.n_step_td_losses[i] = tf.reduce_mean(
self.importance_weights * nstep_td_error) * self.lambda_nstep
self.td_errors[i] = td_error + nstep_td_error
self.critic_losses[i] = self.step_1_td_losses[i] + \
self.n_step_td_losses[i]
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critics[i].trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_losses[i] += critic_reg
critic_shapes = [
var.get_shape().as_list()
for var in self.critics[i].trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads[i] = U.flatgrad(
self.critic_losses[i],
self.critics[i].trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizers[i] = MpiAdam(
var_list=self.critics[i].trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_summaries(self):
tf.summary.scalar("actor_loss", self.actor_loss)
for i in range(self.num_critics):
name_sufffix = str(i)
tf.summary.scalar("critic_loss" + name_sufffix,
self.critic_losses[i])
tf.summary.scalar("1step_loss" + name_sufffix,
self.step_1_td_losses[i])
tf.summary.scalar("nstep_loss" + name_sufffix,
self.n_step_td_losses[i])
tf.summary.scalar("percentage_of_demonstrations",
tf.reduce_sum(self.pretraining_tf) / self.batch_size)
tf.summary.scalar("number_of_demos_better_than_actor",
self.number_of_demos_better)
tf.summary.histogram("pretrain_samples", self.pretraining_tf)
tf.summary.scalar("bc_loss", self.bc_loss)
tf.summary.scalar("obj_conf_loss", self.obj_conf_loss)
tf.summary.scalar("target_loss", self.target_loss)
tf.summary.scalar("gripper_loss", self.gripper_loss)
tf.summary.scalar("original_actor_loss", self.original_actor_loss)
tf.summary.scalar("memory_size", self.memory_size)
tf.summary.scalar("rss", self.rss)
self.scalar_summaries = tf.summary.merge_all()
# reward
self.r_plot_in = tf.placeholder(tf.float32, name='r_plot_in')
self.r_plot = tf.summary.scalar("returns", self.r_plot_in)
self.r_plot_in_eval = tf.placeholder(tf.float32, name='r_plot_in_eval')
self.r_plot_eval = tf.summary.scalar("returns_eval",
self.r_plot_in_eval)
self.obj_conf_in_eval = tf.placeholder(
tf.float32, name='obj_conf_in_eval')
self.obj_conf_eval = tf.summary.scalar("obj_conf_eval",
self.obj_conf_in_eval)
self.grip_in_eval = tf.placeholder(tf.float32, name='grip_in_eval')
self.grip_eval = tf.summary.scalar("grip_eval", self.grip_in_eval)
self.target_in_eval = tf.placeholder(tf.float32, name='target_in_eval')
self.target_eval = tf.summary.scalar("target_eval",
self.target_in_eval)
self.writer = tf.summary.FileWriter(
tmp + '/summaries/' + self.run_name, graph=tf.get_default_graph())
def save_reward(self, r):
self.ep += 1
summary = self.sess.run(self.r_plot, feed_dict={self.r_plot_in: r})
self.writer.add_summary(summary, self.ep)
def save_aux_prediction(self, obj_conf, grip, target):
self.ep += 1
obj_conf_summ, grip_summ, target_summ = self.sess.run(
[self.obj_conf_eval, self.grip_eval, self.target_eval],
feed_dict={
self.obj_conf_in_eval: obj_conf,
self.grip_in_eval: grip,
self.target_in_eval: target
})
self.writer.add_summary(obj_conf_summ, self.ep)
self.writer.add_summary(grip_summ, self.ep)
self.writer.add_summary(target_summ, self.ep)
def save_eval_reward(self, r, ep):
summary = self.sess.run(
self.r_plot_eval, feed_dict={self.r_plot_in_eval: r})
self.writer.add_summary(summary, ep)
def setup_stats(self):
with tf.name_scope('stats'):
ops = []
names = []
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tfs[0])]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tfs[0])]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tfs[0])]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tfs[0])]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, aux, state0, apply_noise=True, compute_Q=True):
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs], self.aux0: [aux], self.state0: [state0]}
if compute_Q:
action, q, obj_conf, gripper, target = self.sess.run(
[
actor_tf, self.critic_with_actor_tfs[0], self.obj_conf,
self.gripper, self.target
],
feed_dict=feed_dict)
else:
action, obj_conf, gripper, target = self.sess.run(
[actor_tf, self.obj_conf, self.gripper, self.target],
feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, obj_conf, gripper, target
def store_transition(self,
state,
obs0,
action,
reward,
state1,
obs1,
terminal1,
aux0,
aux1,
i,
demo=False):
reward *= self.reward_scale
if demo:
self.memory.append_demonstration(state, obs0, action, reward,
state1, obs1, terminal1, aux0, aux1, i)
else:
assert i is None
self.memory.append(state, obs0, action, reward, state1, obs1,
terminal1, aux0, aux1, i)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
if self.normalize_state:
self.state_rms.update(np.array([state]))
if self.normalize_aux:
self.aux_rms.update(np.array([aux0]))
def train(self, iteration, pretrain=False):
batch, n_step_batch, percentage = self.memory.sample_rollout(
batch_size=self.batch_size,
nsteps=self.nsteps,
beta=self.replay_beta,
gamma=self.gamma,
pretrain=pretrain)
target_Q_1step = self.sess.run(
self.target_Q,
feed_dict={
self.obs1: batch['obs1'],
self.state1: batch['states1'],
self.aux1: batch['aux1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
self.nstep_steps: np.ones((self.batch_size, 1)),
})
target_Q_nstep = self.sess.run(
self.target_Q,
feed_dict={
self.obs1: n_step_batch['obs1'],
self.state1: n_step_batch['states1'],
self.aux1: n_step_batch['aux1'],
self.rewards: n_step_batch['rewards'],
self.nstep_steps: n_step_batch['step_reached'],
self.terminals1: n_step_batch['terminals1'].astype('float32'),
})
critic_grads = [None] * self.num_critics
critic_losses = [None] * self.num_critics
td_errors = [None] * self.num_critics
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, *self.critic_grads,
*self.critic_losses, *self.td_errors, self.scalar_summaries
]
ret = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.importance_weights: batch['weights'],
self.state0: batch['states0'],
self.aux0: batch['aux0'],
self.actions: batch['actions'],
self.critic_target: target_Q_1step,
self.nstep_critic_target: target_Q_nstep,
self.pretraining_tf: batch['demos'].astype('float32'),
self.memory_size: len(self.memory.storage),
self.rss: resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
})
if self.num_critics == 2:
actor_grads, actor_loss, critic_grads[0], critic_grads[1], critic_losses[0], critic_losses[1], td_errors[0], \
td_errors[1], scalar_summaries = ret
else:
actor_grads, actor_loss, critic_grads[0], critic_losses[
0], td_errors[0], scalar_summaries = ret
self.memory.update_priorities(batch['idxes'], td_errors[0])
for i in range(self.num_critics):
self.critic_optimizers[i].update(
critic_grads[i], stepsize=self.critic_lr)
self.writer.add_summary(scalar_summaries, iteration)
if iteration % self.policy_and_target_update_period == 0:
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
return critic_losses[0], actor_loss
def set_sess(self, sess):
self.sess = sess
def initialize_vars(self):
self.sess.run(tf.global_variables_initializer())
def sync_optimizers(self):
self.actor_optimizer.sync()
for i in range(self.num_critics):
self.critic_optimizers[i].sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
self.stats_sample = self.memory.sample_prioritized(
batch_size=self.batch_size, replay_beta=self.replay_beta)
values = self.sess.run(
self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
self.aux0: self.stats_sample['aux0'],
self.state0: self.stats_sample['states0'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
return stats
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
def write_summary(self, summary):
agent_summary = {
"gamma": self.gamma,
"tau": self.tau,
"normalize_observations": self.normalize_observations,
"normalize_state": self.normalize_state,
"normalize_aux": self.normalize_aux,
"action_noise": self.action_noise,
"action_range": self.action_range,
"return_range": self.return_range,
"observation_range": self.observation_range,
"actor_lr": self.actor_lr,
"state_range": self.state_range,
"critic_lr": self.critic_lr,
"clip_norm": self.clip_norm,
"enable_popart": self.enable_popart,
"reward_scale": self.reward_scale,
"batch_size": self.batch_size,
"critic_l2_reg": self.critic_l2_reg,
"lambda_nstep": self.lambda_nstep,
"lambda_1step": self.lambda_1step,
"nsteps": self.nsteps,
"replay_beta": self.replay_beta,
"run_name": self.run_name,
"lambda_pretrain": self.lambda_pretrain,
"target_policy_noise": self.target_policy_noise,
"target_policy_noise_clip": self.target_policy_noise_clip,
"lambda_obj_conf_predict": self.lambda_obj_conf_predict,
"lambda_target_predict": self.lambda_target_predict,
"lambda_gripper_predict": self.lambda_gripper_predict,
}
summary["agent_summary"] = agent_summary
md_string = self._markdownize_summary(summary)
summary_op = tf.summary.text("param_info",
tf.convert_to_tensor(md_string))
text = self.sess.run(summary_op)
self.writer.add_summary(text)
self.writer.flush()
print(md_string)
@staticmethod
def _markdownize_summary(data):
result = []
for section, params in data.items():
result.append("### " + section)
for param, value in params.items():
result.append("* {} : {}".format(str(param), str(value)))
return "\n".join(result)
class DDPG(object):
def __init__(self,
prefix,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
dis_batch_size=512,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
adaptive_param_noise=True,
adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0.,
actor_lr=1e-4,
actor_dis_lr=1e-4,
critic_lr=1e-3,
exp_scale=1.0,
clip_norm=None,
reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0_' + prefix)
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1_' + prefix)
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1_' + prefix)
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards_' + prefix)
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions_' + prefix)
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target_' + prefix)
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev_' +
prefix)
self.EXP_SCALE = tf.placeholder(tf.float32, [])
# For distillation
#self.dis_obs = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='dis_obs_' + prefix)
self.dis_actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='dis_actions_' + prefix)
self.dis_qs = tf.placeholder(tf.float32,
shape=(None, 1),
name='dis_qs_' + prefix)
self.prefix = prefix
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.actor_dis_lr = actor_dis_lr
self.critic_lr = critic_lr
self.exp_scale = exp_scale
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.dis_batch_size = dis_batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms_' + self.prefix):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms_' + self.prefix):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor_' + self.prefix
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic_' + self.prefix
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(
target_critic(normalized_obs1, target_actor(normalized_obs1)),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_actor_dis_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor_' + self.prefix
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor_' + self.prefix
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_actor_dis_optimizer(self):
logger.info('setting up actor distillation optimizer')
self.weights = tf.stop_gradient(
tf.clip_by_value(
tf.exp(
tf.math.scalar_mul(self.EXP_SCALE,
self.dis_qs - self.critic_tf)), 0.01,
100))
self.weights = self.weights / tf.reduce_sum(self.weights)
self.actor_dis_loss = tf.reduce_sum(
tf.math.multiply(
self.weights,
tf.reduce_mean(tf.square(self.actor_tf - self.dis_actions),
axis=1)))
actor_dis_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
self.actor_dis_grads = U.flatgrad(self.actor_dis_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_dis_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32,
shape=[1],
name='old_std_' + self.prefix)
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32,
shape=[1],
name='old_mean_' + self.prefix)
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def pi_batch(self, obs_batch):
actor_tf = self.actor_tf
feed_dict = {self.obs0: obs_batch}
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
cl0 = critic_loss
al0 = actor_loss
return cl0, al0
def dis_train(self):
if self.partner_agent.memory.nb_entries > 0:
batch = self.partner_agent.memory.sample(
batch_size=self.dis_batch_size)
#print('############ Checking ##############')
#print('Batch: ', batch)
#print('Batch shape: ', batch['obs0'].shape)
obs_batch = batch['obs0']
# Note that, here, the q is denormalized
partner_action_batch, partner_q_batch = self.partner_agent.pi_batch(
obs_batch)
# Actor Distillation
ops = [
self.actor_dis_grads, self.actor_dis_loss, self.weights,
self.critic_tf
]
actor_dis_grads, actor_dis_loss, weights, qs = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.dis_actions: partner_action_batch,
self.actions: partner_action_batch,
self.dis_qs: partner_q_batch,
self.EXP_SCALE: self.exp_scale,
})
#print('########## Checking ###########')
#for i in range(weights.shape[0]):
# print(weights[i], ' ', partner_q_batch[0], ' ', qs[0])
#print('Sum: ', np.sum(weights))
#print('Action Batch: ', action_batch)
#print('Actor Distiallation Loss: ', actor_dis_loss)
#print('Action Batch: ', action_batch)
#print('Q Batch: ', q_batch)
self.actor_dis_optimizer.update(actor_dis_grads,
stepsize=self.actor_dis_lr)
return actor_dis_loss
return 0.0
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
### For dual imitation
def set_partner_agent(self, agent):
self.partner_agent = agent
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
adaptive_param_noise=True,
adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
Q_obs1 = denormalize(
critic(normalized_obs1,
actor(normalized_obs1, reuse=True),
reuse=True), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
self.lag_mult = tf.Variable(1.0,
name='lag_mult',
trainable=True,
import_scope='lag_mult')
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_optimizer(self):
logger.info('setting up actor and critic optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.target_Q, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss +
self.lag_mult * self.critic_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
self.critic_grads = U.flatgrad(self.actor_loss +
self.lag_mult * self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
self.lag_grads = U.flatgrad(
self.actor_loss + self.lag_mult * self.critic_loss,
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='lag_mult'))
self.lag_optimizer = MpiAdam(var_list=tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='lag_mult'),
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.target_Q, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf],
feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss, self.lag_grads, self.lag_mult
]
actor_grads, actor_loss, critic_grads, critic_loss, lag_grads, lag_mult = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
self.lag_optimizer.update(lag_grads, stepsize=self.actor_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.lag_optimizer.sync()
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = mpi_mean(distance)
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class DDPG(object):
def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
# Parameters.
self.gamma = gamma # discount factor
self.tau = tau # stepsize for (smooth) updating the target network weights
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr # learning rate for network of actor
self.critic_lr = critic_lr # learning rate for network of critic
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg # regularization coefficient for network of critic
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Return (= reward) normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1 # TODO: 1- terminals1 ??
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1]) # TODO: maybe comment this line
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
self.old_std : np.array([old_std]),
self.old_mean : np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q, feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
# self.sess.run(tf.global_variables_initializer()) # is done in /baselines/ddpg/training.py
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
# values = [241.93886, 5.4122686, -3.6965165, 0.00028637942, -3.4581754, 2.3841858e-07, 0.25359547, 0.3071089, -0.18079321, 0.5772085]
names = self.stats_names[:] # ['obs_rms_mean', 'obs_rms_std', 'reference_Q_mean', 'reference_Q_std', 'reference_actor_Q_mean', 'reference_actor_Q_std', 'reference_action_mean', 'reference_action_std', 'reference_perturbed...tion_mean', 'reference_perturbed...ction_std']
assert len(names) == len(values)
stats = dict(zip(names, values)) # {'obs_rms_mean': 241.93886, 'obs_rms_std': 5.4122686, 'reference_Q_mean': -3.6965165, 'reference_Q_std': 0.00028637942, 'reference_action_mean': 0.25359547, 'reference_action_std': 0.3071089, 'reference_actor_Q_mean': -3.4581754, 'reference_actor_Q_std': 2.3841858e-07, 'reference_perturbed...tion_mean': -0.18079321, 'reference_perturbed...ction_std': 0.5772085}
if self.param_noise is not None:
stats.update(self.param_noise.get_stats())
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0: batch['obs0'],
self.param_noise_stddev: self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
class DDPG(object):
def __init__(self,
actor,
critic,
memory,
observation_shape,
action_shape,
param_noise=None,
action_noise=None,
gamma=0.99,
tau=0.001,
normalize_returns=False,
enable_popart=False,
normalize_observations=True,
batch_size=128,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
adaptive_param_noise=True,
adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
clip_norm=None,
reward_scale=1.,
sigma=None,
surrogate=False,
expected=False,
sigma_num_samples=10,
random_actor=False,
grad_num_samples=10):
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
self.noises = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='noises')
self.prev_noises = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='prev_noises')
self.critic_target = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.sigma = sigma
self.expected = expected
self.surrogate = surrogate
self.random_actor = random_actor
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0],
self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
if self.sigma is not None and self.action_noise is not None and expected and self.random_actor:
critic_with_actor_tf_list = []
for i in range(grad_num_samples):
# noise = self.action_noise.memory_noise(self.prev_noises)
noise = tf.random_normal(tf.shape(self.actor_tf),
mean=0.0,
stddev=0.2)
noisy_action = self.actor_tf + noise
clipped_action = tf.clip_by_value(noisy_action,
self.action_range[0],
self.action_range[1])
current_critic = critic(normalized_obs0,
clipped_action,
reuse=True)
critic_with_actor_tf = denormalize(
tf.clip_by_value(current_critic, self.return_range[0],
self.return_range[1]), self.ret_rms)
critic_with_actor_tf_list.append(critic_with_actor_tf)
self.critic_with_actor_tf = tf.reduce_mean(tf.concat(
critic_with_actor_tf_list, axis=1),
axis=1,
keepdims=True)
else:
self.normalized_critic_with_actor_tf = critic(normalized_obs0,
self.actor_tf,
reuse=True)
self.critic_with_actor_tf = denormalize(
tf.clip_by_value(self.normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
action = target_actor(normalized_obs1)
if self.sigma is not None and self.action_noise is not None and expected:
# noise = self.action_noise.memory_noise(self.noises, num_samples=num_samples) # preparation for OU noise
Q_obs1_list = []
for i in range(sigma_num_samples):
if i > 0:
reuse = True
else:
reuse = False
# noise = self.action_noise.memory_noise(self.noises)
noise = tf.random_normal(tf.shape(action),
mean=0.0,
stddev=0.2)
noisy_action = action + noise
clipped_action = tf.clip_by_value(noisy_action,
self.action_range[0],
self.action_range[1])
Q_obs1_list.append(
denormalize(
target_critic(normalized_obs1,
clipped_action,
reuse=reuse), self.ret_rms))
Q_obs1 = tf.reduce_mean(tf.concat(Q_obs1_list, axis=1),
axis=1,
keepdims=True)
else:
action = tf.clip_by_value(action, self.action_range[0],
self.action_range[1])
Q_obs1 = denormalize(target_critic(normalized_obs1, action),
self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(
self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(
self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(
self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(
self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(
tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [
var.get_shape().as_list() for var in self.actor.trainable_vars
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss,
self.actor.trainable_vars,
clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(
normalize(self.critic_target, self.ret_rms), self.return_range[0],
self.return_range[1])
self.critic_loss = tf.reduce_mean(
tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [
var for var in self.critic.trainable_vars
if 'kernel' in var.name and 'output' not in var.name
]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(
self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars)
self.critic_loss += critic_reg
critic_shapes = [
var.get_shape().as_list() for var in self.critic.trainable_vars
]
critic_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss,
self.critic.trainable_vars,
clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9,
beta2=0.999,
epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [
M.assign(M * self.old_std / new_std)
]
self.renormalize_Q_outputs_op += [
b.assign(
(b * self.old_std + self.old_mean - new_mean) / new_std)
]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [
tf.reduce_mean(self.obs_rms.mean),
tf.reduce_mean(self.obs_rms.std)
]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
action = self.sess.run(actor_tf, feed_dict=feed_dict)
action = action.flatten()
if self.action_noise is not None and apply_noise:
prev_noise = self.action_noise.prev_noise()
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
else:
noise = None
prev_noise = None
action = np.clip(action, self.action_range[0], self.action_range[1])
if compute_Q:
feed_dict = {self.obs0: [obs], self.actions: [action]}
q = self.sess.run([actor_tf], feed_dict=feed_dict)
else:
q = None
return action, q, noise, prev_noise
def pi_surrogate(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
actor_action = self.sess.run(actor_tf, feed_dict=feed_dict)
actor_action = actor_action.flatten()
if self.action_noise is not None and apply_noise:
prev_noise = self.action_noise.prev_noise()
noise = self.action_noise()
assert noise.shape == actor_action.shape
action = actor_action + noise
else:
noise = None
prev_noise = None
action = np.clip(action, self.action_range[0], self.action_range[1])
if compute_Q:
feed_dict = {self.obs0: [obs], self.actions: [action]}
q = self.sess.run([actor_tf], feed_dict=feed_dict)
else:
q = None
return action, q, noise, prev_noise, actor_action
#
# def pi(self, obs, apply_noise=True, compute_Q=True):
# if self.param_noise is not None and apply_noise:
# actor_tf = self.perturbed_actor_tf
# else:
# actor_tf = self.actor_tf
# feed_dict = {self.obs0: [obs]}
# action = self.sess.run(actor_tf, feed_dict=feed_dict)
# action = action.flatten()
# if self.action_noise is not None and apply_noise:
# noise = self.action_noise()
# assert noise.shape == action.shape
# else:
# noise = None
# action = np.clip(action, self.action_range[0], self.action_range[1])
# if compute_Q:
# feed_dict = {self.obs0: [obs], self.actions: [action]}
# q = self.sess.run([actor_tf], feed_dict=feed_dict)
# else:
# q = None
# if self.action_noise is not None and apply_noise:
# noise = self.action_noise()
# assert noise.shape == action.shape
# action += noise
# action = np.clip(action, self.action_range[0], self.action_range[1])
# return action , q, noise
# def pi(self, obs, apply_noise=True, compute_Q=True):
# if self.param_noise is not None and apply_noise:
# actor_tf = self.perturbed_actor_tf
# else:
# actor_tf = self.actor_tf
# feed_dict = {self.obs0: [obs]}
# if compute_Q:
# self.sess.run
# action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
# else:
# action = self.sess.run(actor_tf, feed_dict=feed_dict)
# q = None
# action = action.flatten()
# if self.action_noise is not None and apply_noise:
# noise = self.action_noise()
# assert noise.shape == action.shape
# action += noise
# action = np.clip(action, self.action_range[0], self.action_range[1])
# return action, q, noise
def store_transition(self, obs0, action, reward, obs1, terminal1, noise,
prev_noise):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1, noise,
prev_noise)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run(
[self.ret_rms.mean, self.ret_rms.std, self.target_Q],
feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
self.noises: batch['noises'],
self.prev_noises: batch['prev_noises']
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op,
feed_dict={
self.old_std: np.array([old_std]),
self.old_mean: np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q,
feed_dict={
self.obs1:
batch['obs1'],
self.rewards:
batch['rewards'],
self.terminals1:
batch['terminals1'].astype('float32'),
self.noises:
batch['noises'],
self.prev_noises:
batch['prev_noises']
})
# Get all gradients and perform a synced update.
ops = [
self.actor_grads, self.actor_loss, self.critic_grads,
self.critic_loss
]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(
ops,
feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.noises: batch['noises'],
self.prev_noises: batch['prev_noises'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops,
feed_dict={
self.obs0:
self.stats_sample['obs0'],
self.actions:
self.stats_sample['actions'],
self.noises:
self.stats_sample['noises'],
self.prev_noises:
self.stats_sample['prev_noises']
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance,
feed_dict={
self.obs0:
batch['obs0'],
self.param_noise_stddev:
self.param_noise.current_stddev,
})
mean_distance = MPI.COMM_WORLD.allreduce(
distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops,
feed_dict={
self.param_noise_stddev:
self.param_noise.current_stddev,
})
class DDPG(object):
def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
# Inputs.
self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
# Parameters.
self.gamma = gamma
self.tau = tau
self.memory = memory
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.critic = critic
self.actor = actor
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
self.observation_range[0], self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across setup parts.
self.actor_tf = actor(normalized_obs0)
self.normalized_critic_tf = critic(normalized_obs0, self.actions)
self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
def setup_target_network_updates(self):
actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
self.target_init_updates = [actor_init_updates, critic_init_updates]
self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
def setup_param_noise(self, normalized_obs0):
assert self.param_noise is not None
# Configure perturbed actor.
param_noise_actor = copy(self.actor)
param_noise_actor.name = 'param_noise_actor'
self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
logger.info('setting up param noise')
self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
# Configure separate copy for stddev adoption.
adaptive_param_noise_actor = copy(self.actor)
adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
def setup_actor_optimizer(self):
logger.info('setting up actor optimizer')
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
logger.info(' actor shapes: {}'.format(actor_shapes))
logger.info(' actor params: {}'.format(actor_nb_params))
self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_critic_optimizer(self):
logger.info('setting up critic optimizer')
normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
if self.critic_l2_reg > 0.:
critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
for var in critic_reg_vars:
logger.info(' regularizing: {}'.format(var.name))
logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg))
critic_reg = tc.layers.apply_regularization(
tc.layers.l2_regularizer(self.critic_l2_reg),
weights_list=critic_reg_vars
)
self.critic_loss += critic_reg
critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
logger.info(' critic shapes: {}'.format(critic_shapes))
logger.info(' critic params: {}'.format(critic_nb_params))
self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
beta1=0.9, beta2=0.999, epsilon=1e-08)
def setup_popart(self):
# See https://arxiv.org/pdf/1602.07714.pdf for details.
self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
new_std = self.ret_rms.std
self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
new_mean = self.ret_rms.mean
self.renormalize_Q_outputs_op = []
for vs in [self.critic.output_vars, self.target_critic.output_vars]:
assert len(vs) == 2
M, b = vs
assert 'kernel' in M.name
assert 'bias' in b.name
assert M.get_shape()[-1] == 1
assert b.get_shape()[-1] == 1
self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
def setup_stats(self):
ops = []
names = []
if self.normalize_returns:
ops += [self.ret_rms.mean, self.ret_rms.std]
names += ['ret_rms_mean', 'ret_rms_std']
if self.normalize_observations:
ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
names += ['obs_rms_mean', 'obs_rms_std']
ops += [tf.reduce_mean(self.critic_tf)]
names += ['reference_Q_mean']
ops += [reduce_std(self.critic_tf)]
names += ['reference_Q_std']
ops += [tf.reduce_mean(self.critic_with_actor_tf)]
names += ['reference_actor_Q_mean']
ops += [reduce_std(self.critic_with_actor_tf)]
names += ['reference_actor_Q_std']
ops += [tf.reduce_mean(self.actor_tf)]
names += ['reference_action_mean']
ops += [reduce_std(self.actor_tf)]
names += ['reference_action_std']
if self.param_noise:
ops += [tf.reduce_mean(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_mean']
ops += [reduce_std(self.perturbed_actor_tf)]
names += ['reference_perturbed_action_std']
self.stats_ops = ops
self.stats_names = names
def pi(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: [obs]}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
action = action.flatten()
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action.shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q
def store_transition(self, obs0, action, reward, obs1, terminal1):
reward *= self.reward_scale
self.memory.append(obs0, action, reward, obs1, terminal1)
if self.normalize_observations:
self.obs_rms.update(np.array([obs0]))
def train(self):
# Get a batch.
batch = self.memory.sample(batch_size=self.batch_size)
if self.normalize_returns and self.enable_popart:
old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
self.ret_rms.update(target_Q.flatten())
self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
self.old_std : np.array([old_std]),
self.old_mean : np.array([old_mean]),
})
# Run sanity check. Disabled by default since it slows down things considerably.
# print('running sanity check')
# target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
# self.obs1: batch['obs1'],
# self.rewards: batch['rewards'],
# self.terminals1: batch['terminals1'].astype('float32'),
# })
# print(target_Q_new, target_Q, new_mean, new_std)
# assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
else:
target_Q = self.sess.run(self.target_Q, feed_dict={
self.obs1: batch['obs1'],
self.rewards: batch['rewards'],
self.terminals1: batch['terminals1'].astype('float32'),
})
# Get all gradients and perform a synced update.
ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
self.obs0: batch['obs0'],
self.actions: batch['actions'],
self.critic_target: target_Q,
})
self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
return critic_loss, actor_loss
def initialize(self, sess):
self.sess = sess
self.sess.run(tf.global_variables_initializer())
self.actor_optimizer.sync()
self.critic_optimizer.sync()
self.sess.run(self.target_init_updates)
def update_target_net(self):
self.sess.run(self.target_soft_updates)
def get_stats(self):
if self.stats_sample is None:
# Get a sample and keep that fixed for all further computations.
# This allows us to estimate the change in value for the same set of inputs.
self.stats_sample = self.memory.sample(batch_size=self.batch_size)
values = self.sess.run(self.stats_ops, feed_dict={
self.obs0: self.stats_sample['obs0'],
self.actions: self.stats_sample['actions'],
})
names = self.stats_names[:]
assert len(names) == len(values)
stats = dict(zip(names, values))
if self.param_noise is not None:
stats = {**stats, **self.param_noise.get_stats()}
return stats
def adapt_param_noise(self):
if self.param_noise is None:
return 0.
# Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
batch = self.memory.sample(batch_size=self.batch_size)
self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
self.obs0: batch['obs0'],
self.param_noise_stddev: self.param_noise.current_stddev,
})
mean_distance = mpi_mean(distance)
self.param_noise.adapt(mean_distance)
return mean_distance
def reset(self):
# Reset internal state after an episode is complete.
if self.action_noise is not None:
self.action_noise.reset()
if self.param_noise is not None:
self.sess.run(self.perturb_policy_ops, feed_dict={
self.param_noise_stddev: self.param_noise.current_stddev,
})
