class DDPG(object):
@store_args
def __init__(self,
input_dims,
buffer_size,
hidden,
layers,
network_class_actor_critic,
network_class_discriminator,
polyak,
batch_size,
Q_lr,
pi_lr,
mi_lr,
sk_lr,
r_scale,
mi_r_scale,
sk_r_scale,
et_r_scale,
norm_eps,
norm_clip,
max_u,
action_l2,
clip_obs,
scope,
T,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
sample_transitions,
gamma,
env_name,
max_timesteps,
pretrain_weights,
finetune_pi,
mi_prioritization,
sac,
reuse=False,
history_len=10000,
**kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(
self.network_class_actor_critic)
self.create_discriminator = import_function(
self.network_class_discriminator)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimz = self.input_dims['z']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
self.env_name = env_name
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None, )
stage_shapes['w'] = (None, )
stage_shapes['m'] = (None, )
stage_shapes['s'] = (None, )
stage_shapes['m_w'] = ()
stage_shapes['s_w'] = ()
stage_shapes['r_w'] = ()
stage_shapes['e_w'] = ()
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(pretrain_weights,
mi_prioritization,
reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {
key: (self.T if key != 'o' else self.T + 1, *input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T + 1, self.dimg)
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T,
self.sample_transitions, mi_prioritization)
self.mi_r_history = deque(maxlen=history_len)
self.gl_r_history = deque(maxlen=history_len)
self.sk_r_history = deque(maxlen=history_len)
self.et_r_history = deque(maxlen=history_len)
self.mi_current = 0
self.finetune_pi = finetune_pi
def _random_action(self, n):
return np.random.uniform(low=-self.max_u,
high=self.max_u,
size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def get_actions(self,
o,
z,
ag,
g,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main
# values to compute
if self.sac:
vals = [policy.mu_tf]
else:
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
feed = {
policy.o_tf:
o.reshape(-1, self.dimo),
policy.z_tf:
z.reshape(-1, self.dimz),
policy.g_tf:
g.reshape(-1, self.dimg),
policy.u_tf:
np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(
*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (
self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
# update the mutual information reward into the episode batch
episode_batch['m'] = np.empty([episode_batch['o'].shape[0], 1])
episode_batch['s'] = np.empty([episode_batch['o'].shape[0], 1])
# #
self.buffer.store_episode(episode_batch, self)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
transitions = self.sample_transitions(self, False, episode_batch,
num_normalizing_transitions,
0, 0, 0)
o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions[
'g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
self.mi_adam.sync()
self.sk_adam.sync()
def _grads_mi(self, data):
mi, mi_grad = self.sess.run([
self.main_ir.mi_tf,
self.mi_grad_tf,
],
feed_dict={self.o_tau_tf: data})
return mi, mi_grad
def _grads_sk(self, o_s_batch, z_s_batch):
sk, sk_grad = self.sess.run([
self.main_ir.sk_tf,
self.sk_grad_tf,
],
feed_dict={
self.main_ir.o_tf: o_s_batch,
self.main_ir.z_tf: z_s_batch
})
return sk, sk_grad
def _grads(self):
critic_loss, actor_loss, Q_grad, pi_grad, neg_logp_pi, e_w = self.sess.run(
[
self.Q_loss_tf,
self.main.Q_pi_tf,
self.Q_grad_tf,
self.pi_grad_tf,
self.main.neg_logp_pi_tf,
self.e_w_tf,
])
return critic_loss, actor_loss, Q_grad, pi_grad, neg_logp_pi, e_w
def _update_mi(self, mi_grad):
self.mi_adam.update(mi_grad, self.mi_lr)
def _update_sk(self, sk_grad):
self.sk_adam.update(sk_grad, self.sk_lr)
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self, ir, t):
transitions = self.buffer.sample(self, ir, self.batch_size,
self.mi_r_scale, self.sk_r_scale, t)
weights = np.ones_like(transitions['r']).copy()
if ir:
self.mi_r_history.extend(
((np.clip((self.mi_r_scale * transitions['m']), *(0, 1)) -
(1 if not self.mi_r_scale == 0 else 0)) *
transitions['m_w']).tolist())
self.sk_r_history.extend(
((np.clip(self.sk_r_scale * transitions['s'], *(-1, 0))) *
1.00).tolist())
self.gl_r_history.extend(self.r_scale * transitions['r'])
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(
o_2, ag_2, g)
transitions['w'] = weights.flatten().copy() # note: ordered dict
transitions_batch = [
transitions[key] for key in self.stage_shapes.keys()
]
return transitions_batch
def stage_batch(self, ir, t, batch=None):
if batch is None:
batch = self.sample_batch(ir, t)
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def run_mi(self, o_s):
feed_dict = {self.o_tau_tf: o_s.copy()}
neg_l = self.sess.run(self.main_ir.mi_tf, feed_dict=feed_dict)
return neg_l
def run_sk(self, o, z):
feed_dict = {self.main_ir.o_tf: o, self.main_ir.z_tf: z}
sk_r = self.sess.run(self.main_ir.sk_r_tf, feed_dict=feed_dict)
return sk_r
def train_mi(self, data, stage=True):
mi, mi_grad = self._grads_mi(data)
self._update_mi(mi_grad)
self.mi_current = -mi.mean()
return -mi.mean()
def train_sk(self, o_s_batch, z_s_batch, stage=True):
sk, sk_grad = self._grads_sk(o_s_batch, z_s_batch)
self._update_sk(sk_grad)
return -sk.mean()
def train(self, t, stage=True):
if not self.buffer.current_size == 0:
if stage:
self.stage_batch(ir=True, t=t)
critic_loss, actor_loss, Q_grad, pi_grad, neg_logp_pi, e_w = self._grads(
)
self._update(Q_grad, pi_grad)
self.et_r_history.extend(((np.clip(
(self.et_r_scale * neg_logp_pi), *(-1, 0))) * e_w).tolist())
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _create_network(self,
pretrain_weights,
mi_prioritization,
reuse=False):
if self.sac:
logger.info("Creating a SAC agent with action space %d x %s..." %
(self.dimu, self.max_u))
else:
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dimu, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([
(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
batch_tf['w'] = tf.reshape(batch_tf['w'], [-1, 1])
batch_tf['m'] = tf.reshape(batch_tf['m'], [-1, 1])
batch_tf['s'] = tf.reshape(batch_tf['s'], [-1, 1])
self.o_tau_tf = tf.placeholder(tf.float32,
shape=(None, None, self.dimo))
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf,
net_type='main',
**self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(target_batch_tf,
net_type='target',
**self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# intrinsic reward (ir) network for mutual information
with tf.variable_scope('ir') as vs:
if reuse:
vs.reuse_variables()
self.main_ir = self.create_discriminator(batch_tf,
net_type='ir',
**self.__dict__)
vs.reuse_variables()
# loss functions
mi_grads_tf = tf.gradients(tf.reduce_mean(self.main_ir.mi_tf),
self._vars('ir/state_mi'))
assert len(self._vars('ir/state_mi')) == len(mi_grads_tf)
self.mi_grads_vars_tf = zip(mi_grads_tf, self._vars('ir/state_mi'))
self.mi_grad_tf = flatten_grads(grads=mi_grads_tf,
var_list=self._vars('ir/state_mi'))
self.mi_adam = MpiAdam(self._vars('ir/state_mi'),
scale_grad_by_procs=False)
sk_grads_tf = tf.gradients(tf.reduce_mean(self.main_ir.sk_tf),
self._vars('ir/skill_ds'))
assert len(self._vars('ir/skill_ds')) == len(sk_grads_tf)
self.sk_grads_vars_tf = zip(sk_grads_tf, self._vars('ir/skill_ds'))
self.sk_grad_tf = flatten_grads(grads=sk_grads_tf,
var_list=self._vars('ir/skill_ds'))
self.sk_adam = MpiAdam(self._vars('ir/skill_ds'),
scale_grad_by_procs=False)
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return,
self.clip_return if self.clip_pos_returns else np.inf)
self.e_w_tf = batch_tf['e_w']
if not self.sac:
self.main.neg_logp_pi_tf = tf.zeros(1)
target_tf = tf.clip_by_value(
self.r_scale * batch_tf['r'] * batch_tf['r_w'] +
(tf.clip_by_value(self.mi_r_scale * batch_tf['m'], *(0, 1)) -
(1 if not self.mi_r_scale == 0 else 0)) * batch_tf['m_w'] +
(tf.clip_by_value(self.sk_r_scale * batch_tf['s'], *(-1, 0))) *
batch_tf['s_w'] +
(tf.clip_by_value(self.et_r_scale * self.main.neg_logp_pi_tf,
*(-1, 0))) * self.e_w_tf +
self.gamma * target_Q_pi_tf, *clip_range)
self.td_error_tf = tf.stop_gradient(target_tf) - self.main.Q_tf
self.errors_tf = tf.square(self.td_error_tf)
self.errors_tf = tf.reduce_mean(batch_tf['w'] * self.errors_tf)
self.Q_loss_tf = tf.reduce_mean(self.errors_tf)
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf,
var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf,
var_list=self._vars('main/pi'))
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=False)
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
# polyak averaging
self.stats_vars = self._global_vars('o_stats') + self._global_vars(
'g_stats')
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]),
zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
if pretrain_weights:
load_weight(self.sess, pretrain_weights, ['state_mi'])
if self.finetune_pi:
load_weight(self.sess, pretrain_weights, ['main'])
self._sync_optimizers()
if pretrain_weights and self.finetune_pi:
load_weight(self.sess, pretrain_weights, ['target'])
else:
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
logs += [('mi_reward/mean', np.mean(self.mi_r_history))]
logs += [('mi_reward/std', np.std(self.mi_r_history))]
logs += [('mi_reward/max', np.max(self.mi_r_history))]
logs += [('mi_reward/min', np.min(self.mi_r_history))]
logs += [('mi_train/-neg_l', self.mi_current)]
logs += [('sk_reward/mean', np.mean(self.sk_r_history))]
logs += [('sk_reward/std', np.std(self.sk_r_history))]
logs += [('sk_reward/max', np.max(self.sk_r_history))]
logs += [('sk_reward/min', np.min(self.sk_r_history))]
logs += [('et_reward/mean', np.mean(self.et_r_history))]
logs += [('et_reward/std', np.std(self.et_r_history))]
logs += [('et_reward/max', np.max(self.et_r_history))]
logs += [('et_reward/min', np.min(self.et_r_history))]
logs += [('gl_reward/mean', np.mean(self.gl_r_history))]
logs += [('gl_reward/std', np.std(self.gl_r_history))]
logs += [('gl_reward/max', np.max(self.gl_r_history))]
logs += [('gl_reward/min', np.min(self.gl_r_history))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main',
'target', 'lock', 'sample_transitions', 'stage_shapes',
'create_actor_critic', 'create_discriminator', '_history'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([not subname in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
if 'env_name' not in state:
state['env_name'] = 'FetchPickAndPlace-v1'
if 'network_class_discriminator' not in state:
state[
'network_class_discriminator'] = 'baselines.her.discriminator:Discriminator'
if 'mi_r_scale' not in state:
state['mi_r_scale'] = 1
if 'mi_lr' not in state:
state['mi_lr'] = 0.001
if 'sk_r_scale' not in state:
state['sk_r_scale'] = 1
if 'sk_lr' not in state:
state['sk_lr'] = 0.001
if 'et_r_scale' not in state:
state['et_r_scale'] = 1
if 'finetune_pi' not in state:
state['finetune_pi'] = None
if 'no_train_mi' not in state:
state['no_train_mi'] = None
if 'load_weight' not in state:
state['load_weight'] = None
if 'pretrain_weights' not in state:
state['pretrain_weights'] = None
if 'mi_prioritization' not in state:
state['mi_prioritization'] = None
if 'sac' not in state:
state['sac'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert (len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
class DDPG(object):
@store_args
def __init__(self,
input_dims,
buffer_size,
hidden,
layers,
network_class,
polyak,
batch_size,
Q_lr,
pi_lr,
norm_eps,
norm_clip,
max_u,
action_l2,
clip_obs,
scope,
T,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
sample_transitions,
gamma,
temperature,
prioritization,
env_name,
alpha,
beta0,
beta_iters,
eps,
max_timesteps,
rank_method,
reuse=False,
**kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
self.prioritization = prioritization
self.env_name = env_name
self.temperature = temperature
self.rank_method = rank_method
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None, )
stage_shapes['w'] = (None, )
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {
key: (self.T if key != 'o' else self.T + 1, *input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T + 1, self.dimg)
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
if self.prioritization == 'entropy':
self.buffer = ReplayBufferEntropy(buffer_shapes, buffer_size,
self.T, self.sample_transitions,
self.prioritization,
self.env_name)
elif self.prioritization == 'tderror':
self.buffer = PrioritizedReplayBuffer(buffer_shapes, buffer_size,
self.T,
self.sample_transitions,
alpha, self.env_name)
if beta_iters is None:
beta_iters = max_timesteps
self.beta_schedule = LinearSchedule(beta_iters,
initial_p=beta0,
final_p=1.0)
else:
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T,
self.sample_transitions)
def _random_action(self, n):
return np.random.uniform(low=-self.max_u,
high=self.max_u,
size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def get_actions(self,
o,
ag,
g,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf:
o.reshape(-1, self.dimo),
policy.g_tf:
g.reshape(-1, self.dimg),
policy.u_tf:
np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(
*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (
self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def get_td_errors(self, o, g, u):
o, g = self._preprocess_og(o, g, g)
vals = [self.td_error_tf]
r = np.ones((o.reshape(-1, self.dimo).shape[0], 1))
feed = {
self.target.o_tf: o.reshape(-1, self.dimo),
self.target.g_tf: g.reshape(-1, self.dimg),
self.bath_tf_r: r,
self.main.o_tf: o.reshape(-1, self.dimo),
self.main.g_tf: g.reshape(-1, self.dimg),
self.main.u_tf: u.reshape(-1, self.dimu)
}
td_errors = self.sess.run(vals, feed_dict=feed)
td_errors = td_errors.copy()
return td_errors
def fit_density_model(self):
self.buffer.fit_density_model()
def store_episode(self,
episode_batch,
dump_buffer,
rank_method,
epoch,
update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
if self.prioritization == 'tderror':
self.buffer.store_episode(episode_batch, dump_buffer)
elif self.prioritization == 'entropy':
self.buffer.store_episode(episode_batch, rank_method, epoch)
else:
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
if self.prioritization == 'entropy':
if not self.buffer.current_size == 0 and not len(
episode_batch['ag']) == 0:
transitions = self.sample_transitions(
episode_batch, num_normalizing_transitions, 'none',
1.0, True)
elif self.prioritization == 'tderror':
transitions, weights, episode_idxs = \
self.sample_transitions(self.buffer, episode_batch, num_normalizing_transitions, beta=0)
else:
transitions = self.sample_transitions(
episode_batch, num_normalizing_transitions)
o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions[
'g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def dump_buffer(self, epoch):
self.buffer.dump_buffer(epoch)
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad, td_error = self.sess.run([
self.Q_loss_tf, self.main.Q_pi_tf, self.Q_grad_tf, self.pi_grad_tf,
self.td_error_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad, td_error
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self, t):
if self.prioritization == 'entropy':
transitions = self.buffer.sample(self.batch_size,
self.rank_method,
temperature=self.temperature)
weights = np.ones_like(transitions['r']).copy()
elif self.prioritization == 'tderror':
transitions, weights, idxs = self.buffer.sample(
self.batch_size, beta=self.beta_schedule.value(t))
else:
transitions = self.buffer.sample(self.batch_size)
weights = np.ones_like(transitions['r']).copy()
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(
o_2, ag_2, g)
transitions['w'] = weights.flatten().copy() # note: ordered dict
transitions_batch = [
transitions[key] for key in self.stage_shapes.keys()
]
if self.prioritization == 'tderror':
return (transitions_batch, idxs)
else:
return transitions_batch
def stage_batch(self, t, batch=None):
if batch is None:
if self.prioritization == 'tderror':
batch, idxs = self.sample_batch(t)
else:
batch = self.sample_batch(t)
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
if self.prioritization == 'tderror':
return idxs
def train(self, t, dump_buffer, stage=True):
if not self.buffer.current_size == 0:
if stage:
if self.prioritization == 'tderror':
idxs = self.stage_batch(t)
else:
self.stage_batch(t)
critic_loss, actor_loss, Q_grad, pi_grad, td_error = self._grads()
if self.prioritization == 'tderror':
new_priorities = np.abs(td_error) + self.eps # td_error
if dump_buffer:
T = self.buffer.buffers['u'].shape[1]
episode_idxs = idxs // T
t_samples = idxs % T
batch_size = td_error.shape[0]
with self.buffer.lock:
for i in range(batch_size):
self.buffer.buffers['td'][episode_idxs[i]][
t_samples[i]] = td_error[i]
self.buffer.update_priorities(idxs, new_priorities)
self._update(Q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dimu, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([
(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
batch_tf['w'] = tf.reshape(batch_tf['w'], [-1, 1])
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf,
net_type='main',
**self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(target_batch_tf,
net_type='target',
**self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return,
0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(
batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
self.td_error_tf = tf.stop_gradient(target_tf) - self.main.Q_tf
self.errors_tf = tf.square(self.td_error_tf)
self.errors_tf = tf.reduce_mean(batch_tf['w'] * self.errors_tf)
self.Q_loss_tf = tf.reduce_mean(self.errors_tf)
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf,
var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf,
var_list=self._vars('main/pi'))
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=False)
# polyak averaging
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars(
'g_stats')
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]),
zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main',
'target', 'lock', 'env', 'sample_transitions', 'stage_shapes',
'create_actor_critic'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([not subname in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
state['env_name'] = None # No need for playing the policy
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert (len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
class DDPG(object):
@store_args
def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size,
Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T,
rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return,
sample_transitions, gamma, reuse=False, **kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None,)
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {key: (self.T if key != 'o' else self.T+1, *input_shapes[key])
for key, val in input_shapes.items()}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T+1, self.dimg)
buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
def _random_action(self, n):
return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g) # clip observations and goals
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf: o.reshape(-1, self.dimo),
policy.g_tf: g.reshape(-1, self.dimg),
policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
# ret = action given by the current policy (eval of NN)
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
# Below: for each mini-batch we take action u (the one given by the policy) with probability
# 1-random_eps, and a random action (u + random_action - u) with probability random_eps
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
self.Q_loss_tf,
self.main.Q_pi_tf,
self.Q_grad_tf,
self.pi_grad_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
transitions = self.buffer.sample(self.batch_size)
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g)
transitions_batch = [transitions[key] for key in self.stage_shapes.keys()]
return transitions_batch
def stage_batch(self, batch=None):
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
if stage:
self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
self._update(Q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(
target_batch_tf, net_type='target', **self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
# self.XX.pi_tf is the action policy we ll use for exploration (TO CONFIRM)
# self.XX.Q_pi_tf is the Q network used to train this policy
# self.XX.Q_tf
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
# target y_i= r + gamma*Q part of the Bellman equation (with returns clipped if necessary:
target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
# loss function for Q_tf where we exclude target_tf from the gradient computation:
self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
# loss function for the action policy is that of the main Q_pi network:
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
# add L2 regularization term from the policy itself:
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
# define the gradients of the Q_loss and pi_loss wrt to their variables respectively
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
# zip the gradients together with their respective variables
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
# flattened gradients and variables
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
# optimizers (using MPI for parralel updates of the network (TO CONFIRM))
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False)
# polyak averaging used for the update of the target networks in both pi and Q nets
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats')
# operation to initialize the target nets at the main nets'values
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars)))
# operation to update the target nets from the main nets using polyak averaging
self.update_target_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]),
zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers() # CHECK WHAT THIS DOES ????
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
'main', 'target', 'lock', 'env', 'sample_transitions',
'stage_shapes', 'create_actor_critic']
state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert(len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
class DDPG(object):
@store_args
def __init__(self, FLAGS, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size,
Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T,
rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return,
bc_loss, q_filter, num_demo, demo_batch_size, prm_loss_weight, aux_loss_weight,
# sample_transitions, gamma, reuse=False, **kwargs):
sample_transitions, gamma, td3_policy_freq, td3_policy_noise, td3_noise_clip, reuse=False, *agent_params, **kwargs): ##
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Added functionality to use demonstrations for training to Overcome exploration problem.
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
bc_loss: whether or not the behavior cloning loss should be used as an auxilliary loss
q_filter: whether or not a filter on the q value update should be used when training with demonstartions
num_demo: Number of episodes in to be used in the demonstration buffer
demo_batch_size: number of samples to be used from the demonstrations buffer, per mpi thread
prm_loss_weight: Weight corresponding to the primary loss
aux_loss_weight: Weight corresponding to the auxilliary loss also called the cloning loss
agent_params: for HAC agent params
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
# self.dimo1= self.input_dims['o1'] ##A.R add for TD3 (has obs0, obs1)
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
#추가된 내용
#parameters for using TD3 variant of DDPG
#https://arxiv.org/abs/1802.09477
self.td3_policy_freq = td3_policy_freq
self.td3_policy_noise = td3_policy_noise
self.td3_noise_clip = td3_noise_clip
## for HAC
self.FLAGS = FLAGS
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
# for key in ['o', 'o1', 'g']: #o1 added by A.R
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None,)
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {key: (self.T-1 if key != 'o' else self.T, *input_shapes[key])
# origin : buffer_shapes = {key: (self.T-1 if key != 'o' else self.T, *input_shapes[key])
# buffer_shapes = {key: (self.T-1 if key != 'o' and key != 'o1' else self.T, *input_shapes[key]) #A.Rㅇ
for key, val in input_shapes.items()}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T, self.dimg)
buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
global DEMO_BUFFER
DEMO_BUFFER = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) #initialize the demo buffer; in the same way as the primary data buffer
print("@ ddgp.py , buffer={}".format(self.buffer))
# self.meta_controller = DDPG(self.dimo + self.dimg, self.dimo, self.clip_obs)
# ##
# self.low_replay_buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
# self.high_replay_buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
# ##
def _random_action(self, n):
return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
# def _preprocess_og(self, o, o1, ag, g): #A.R
if self.relative_goals: ## goal reshape 해주는 곳. ag vs g..흠
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag) #상대적인 골로 만들어 주는구나?..
'''
def simple_goal_subtract(a, b):
assert a.shape == b.shape
return a - b
'''
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
# o1 = np.clip(o1, -self.clip_obs, self.clip_obs) #A.R
g = np.clip(g, -self.clip_obs, self.clip_obs)
# return o, o1, g
return o, g
def step(self, obs):
# FLAGS = FLAGS
actions = self.get_actions(obs['observation'], obs['achieved_goal'], obs['desired_goal'])
# actions = self.get_actions(obs['observation'], obs['achieved_goal'], obs['desired_goal'], FLAGS)
# print("for debug, obs : {}".format(obs['observation']))
return actions, None, None, None
# def get_actions(self, o, o1, ag, g, noise_eps=0., random_eps=0., use_target_net=False, ##o1이 target 네트워크
def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
# def get_actions(self, o, ag, g, FLAGS, noise_eps=0., random_eps=0., use_target_net=False,
compute_Q=False):
# o, o1, g = self._preprocess_og(o, o1, ag, g) ##
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main # rollout.py에서 넘어온다.
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf: o.reshape(-1, self.dimo),
policy.g_tf: g.reshape(-1, self.dimg),
policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def init_demo_buffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer
demoData = np.load(demoDataFile) #load the demonstration data from data file
info_keys = [key.replace('info_', '') for key in self.input_dims.keys() if key.startswith('info_')]
info_values = [np.empty((self.T - 1, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys]
demo_data_obs = demoData['obs']
demo_data_acs = demoData['acs']
demo_data_info = demoData['info']
for epsd in range(self.num_demo): # we initialize the whole demo buffer at the start of the training
obs, acts, goals, achieved_goals = [], [] ,[] ,[]
i = 0
for transition in range(self.T - 1):
obs.append([demo_data_obs[epsd][transition].get('observation')])
acts.append([demo_data_acs[epsd][transition]])
goals.append([demo_data_obs[epsd][transition].get('desired_goal')])
achieved_goals.append([demo_data_obs[epsd][transition].get('achieved_goal')])
for idx, key in enumerate(info_keys):
info_values[idx][transition, i] = demo_data_info[epsd][transition][key]
obs.append([demo_data_obs[epsd][self.T - 1].get('observation')])
achieved_goals.append([demo_data_obs[epsd][self.T - 1].get('achieved_goal')])
episode = dict(o=obs,
u=acts,
g=goals,
ag=achieved_goals)
for key, value in zip(info_keys, info_values):
episode['info_{}'.format(key)] = value
episode = convert_episode_to_batch_major(episode)
global DEMO_BUFFER
DEMO_BUFFER.store_episode(episode) # create the observation dict and append them into the demonstration buffer
logger.debug("Demo buffer size currently ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
if update_stats:
# add transitions to normalizer to normalize the demo data as well
episode['o_2'] = episode['o'][:, 1:, :]
episode['ag_2'] = episode['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode)
transitions = self.sample_transitions(episode, num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
episode.clear()
logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
self.Q_loss_tf,
self.main.Q_pi_tf,
self.Q_grad_tf,
self.pi_grad_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
if self.bc_loss: #use demonstration buffer to sample as well if bc_loss flag is set TRUE
transitions = self.buffer.sample(self.batch_size - self.demo_batch_size)
global DEMO_BUFFER
transitions_demo = DEMO_BUFFER.sample(self.demo_batch_size) #sample from the demo buffer
for k, values in transitions_demo.items():
rolloutV = transitions[k].tolist()
for v in values:
rolloutV.append(v.tolist())
transitions[k] = np.array(rolloutV)
else:
transitions = self.buffer.sample(self.batch_size) #otherwise only sample from primary buffer
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
# o1, o1_2, g = transitions['o1'], transitions['o1_2'] ## A.R
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g)
transitions_batch = [transitions[key] for key in self.stage_shapes.keys()]
print("@ ddpg, sample_batch, transitions_batch={}".format(transitions_batch))
return transitions_batch
def stage_batch(self, batch=None):
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
if stage:
self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads() ## 현재 loss들 가져오는거
self._update(Q_grad, pi_grad) ## 아담 업데이트 하는거
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target1_net_op)
self.sess.run(self.init_target2_net_op)
def update_target_net(self):
# self.sess.run(self.update_target_net_op)
self.sess.run(self.update_target1_net_op)
self.sess.run(self.update_target2_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope)
assert len(res) > 0 #######################이게 왜걸리지? 왜 다시 안걸리지?
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope)
# print("DEBUG, {}".format(res))
return res
def _create_network(self, reuse=False): ## num_demo 추가 -2
logger.info("Debug : Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
self.sess = tf_util.get_session()
# self.num_demo = num_demo
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get() ## 그냥 꺼내오는거..
batch_tf = OrderedDict([(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
#choose only the demo buffer samples
mask = np.concatenate((np.zeros(self.batch_size - self.demo_batch_size), np.ones(self.demo_batch_size)), axis = 0)
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__)
vs.reuse_variables()
print("tf.variable_scope(main) = {}".format(tf.variable_scope('target1'))) #-1
with tf.variable_scope('target1') as vs:
if reuse:
vs.reuse_variables()
target1_batch_tf = batch_tf.copy()
target1_batch_tf['o'] = batch_tf['o_2']
target1_batch_tf['g'] = batch_tf['g_2']
self.target1 = self.create_actor_critic(
target1_batch_tf, net_type='target1', **self.__dict__)
vs.reuse_variables()
print("tf.variable_scope(target1) = {}".format(tf.variable_scope('target1')))
# print("batch= {}".format(target1_batch_tf))
# print(type('target')) #<class 'baselines.her.actor_critic.ActorCritic'>
assert len(self._vars("main")) == len(self._vars("target1"))
with tf.variable_scope('target2') as vs:
if reuse:
vs.reuse_variables()
target2_batch_tf = batch_tf.copy()
target2_batch_tf['o'] = batch_tf['o_2']
target2_batch_tf['g'] = batch_tf['g_2']
self.target2 = self.create_actor_critic(
target2_batch_tf, net_type='target2', **self.__dict__)
vs.reuse_variables()
print("tf.variable_scope(target2) = {}".format(tf.variable_scope('target2')))
print("batch= {}".format(target2_batch_tf))
assert len(self._vars("main")) == len(self._vars("target2"))
for nd in range(self.num_demo):
##A.R
##Compute the target Q value, Q1과 Q2중에 min값을 사용한다.
target1_Q_pi_tf = self.target1.Q_pi_tf ##A.R policy training
target2_Q_pi_tf = self.target2.Q_pi_tf ##A.R
# target_Q_pi_tf = tf.minimum(target1_Q_pi_tf, target2_Q_pi_tf)
# target1_Q_tf = self.target1.Q_tf ##A.R policy training
# target2_Q_tf = self.target2.Q_tf ##A.R
# print('target1={}/////target2={}'.format(target1_Q_tf,target2_Q_tf))
target_Q_pi_tf = tf.minimum(target1_Q_pi_tf, target2_Q_pi_tf)
# target_Q_tf = tf.minimum(target1_Q_tf, target2_Q_tf) ## 대체 코드
# print("{}///{}///{}".format(target1_Q_pi_tf,target2_Q_pi_tf,tf.minimum(target1_Q_pi_tf, target2_Q_pi_tf)))
####
#TD3에서 빠진 코드 :target_Q = reward + (done * discount * target_Q).detach()(L109) ->L428에서 해주고 clip한다
# loss functions
# for policy training, Q_pi_tf = nn(input_Q, [self.hidden] * self.layers + [1])
# target_Q_pi_tf = self.target.Q_pi_tf #original code
clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
target_Q_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
# target_Q_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_tf, *clip_range) ## 대체 코드
# self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
##
# current_Q1, current_Q2 = self.critic(state, action)
# for critic training, Q_tf = nn(input_Q, [self.hidden] * self.layers + [1], reuse=True)
# target_Q_pi_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_tf, *clip_range) #original code
# self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf)) #critic taining
## Get current Q estimates, for critic Q
current_Q1 = self.main.Q_tf ##A.R
current_Q2 = self.main.Q_tf
# print("Q1={}".format(current_Q1))
## Compute critic loss
## Torch => critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)
self.Q_loss_tf = tf.losses.mean_squared_error(current_Q1, target_Q_tf)+ tf.losses.mean_squared_error(current_Q2,target_Q_tf)
# self.Q_loss_tf = tf.losses.mean_squared_error(current_Q1, target_Q_tf)+ tf.losses.mean_squared_error(current_Q2,target_Q_tf)
# print("critic_loss ={}".format(self.Q_loss_tf))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
## Optimize the critic 아담 옵티마이저
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
assert len(self._vars('main/Q')) == len(Q_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
# ## Delayed policy updates
if nd % self.td3_policy_freq == 0:
# print("num_demo = {}".format(nd))
target1_Q_pi_tf = self.target1.Q_pi_tf ##A.R policy training
target2_Q_pi_tf = self.target2.Q_pi_tf ##A.R
tf.print(target1_Q_pi_tf, [target1_Q_pi_tf])
tf.print(target2_Q_pi_tf, [target2_Q_pi_tf])
# print(target2_Q_pi_tf)
target_Q_pi_tf = tf.minimum(target1_Q_pi_tf, target2_Q_pi_tf)
# target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
# Compute actor loss
if self.bc_loss ==1 and self.q_filter == 1 : # train with demonstrations and use bc_loss and q_filter both
maskMain = tf.reshape(tf.boolean_mask(self.main.Q_tf > self.main.Q_pi_tf, mask), [-1]) #where is the demonstrator action better than actor action according to the critic? choose those samples only
#define the cloning loss on the actor's actions only on the samples which adhere to the above masks
self.cloning_loss_tf = tf.reduce_sum(tf.square(tf.boolean_mask(tf.boolean_mask((self.main.pi_tf), mask), maskMain, axis=0) - tf.boolean_mask(tf.boolean_mask((batch_tf['u']), mask), maskMain, axis=0)))
self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(self.main.Q_pi_tf) #primary loss scaled by it's respective weight prm_loss_weight
self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u)) #L2 loss on action values scaled by the same weight prm_loss_weight
self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf #adding the cloning loss to the actor loss as an auxilliary loss scaled by its weight aux_loss_weight
elif self.bc_loss == 1 and self.q_filter == 0: # train with demonstrations without q_filter
self.cloning_loss_tf = tf.reduce_sum(tf.square(tf.boolean_mask((self.main.pi_tf), mask) - tf.boolean_mask((batch_tf['u']), mask)))
self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf
else: #If not training with demonstrations
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
# self.pi_loss_tf = -tf.reduce_mean(self.main.pi_tf) ## what about target1?
# self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
# actor_loss = -tf.reduce_mean(self.main.Q_tf)
# actor_loss += self.action_l2 * tf.reduce_mean(tf.square(self.main.Q_tf / self.max_u))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/pi')) == len(pi_grads_tf)
# Optimize the actor
# Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
# Update the frozen target models
## torch code
# for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
# target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target1_vars = self._vars('target1/Q') + self._vars('target1/pi') ##A.R
self.target2_vars = self._vars('target2/Q') + self._vars('target2/pi') ##A.R
if target_Q_pi_tf == target1_Q_pi_tf:
target_vars = self.target1_vars
else:
target_vars = self.target2_vars
# self.target_vars = self._vars('target/Q') + self._vars('target/pi') #original
self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats')
self.init_target1_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(self.target1_vars, self.main_vars)))
self.init_target2_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(self.target2_vars, self.main_vars)))
self.update_target_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(target_vars, self.main_vars)))
self.update_target1_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(target_vars, self.main_vars)))
self.update_target2_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(target_vars, self.main_vars)))
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
# Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
# pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
# assert len(self._vars('main/Q')) == len(Q_grads_tf)
# assert len(self._vars('main/pi')) == len(pi_grads_tf)
# self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
# self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
# self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
# self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
# optimizers
# self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
# self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False)
# polyak averaging
# self.main_vars = self._vars('main/Q') + self._vars('main/pi')
# self.target1_vars = self._vars('target1/Q') + self._vars('target1/pi') ##A.R
# self.target2_vars = self._vars('target2/Q') + self._vars('target2/pi') ##A.R
# # self.target_vars = self._vars('target/Q') + self._vars('target/pi') #original
# self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats')
# self.init_target1_net_op = list(
# map(lambda v: v[0].assign(v[1]), zip(self.target1_vars, self.main_vars)))
# self.init_target2_net_op = list(
# map(lambda v: v[0].assign(v[1]), zip(self.target2_vars, self.main_vars)))
# self.update_target_net_op = list(
# map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars)))
#original
# self.init_target_net_op = list(
# map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars)))
# self.update_target_net_op = list(
# map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars)))
# # initialize all variables
# tf.variables_initializer(self._global_vars('')).run()
# self._sync_optimizers()
# self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
# 'main', 'target', 'lock', 'env', 'sample_transitions', #original code
'main', 'target1', 'target2', 'lock', 'env', 'sample_transitions',
'stage_shapes', 'create_actor_critic']
state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert(len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
def save(self, save_path):
tf_util.save_variables(save_path)
class DDPG(object):
@store_args
def __init__(self,
input_dims,
buffer_size,
hidden,
layers,
network_class,
polyak,
batch_size,
Q_lr,
pi_lr,
norm_eps,
norm_clip,
max_u,
action_l2,
clip_obs,
scope,
T,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
bc_loss,
q_filter,
num_demo,
demo_batch_size,
prm_loss_weight,
aux_loss_weight,
sample_transitions,
gamma,
reuse=False,
**kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Added functionality to use demonstrations for training to Overcome exploration problem.
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
bc_loss: whether or not the behavior cloning loss should be used as an auxilliary loss
q_filter: whether or not a filter on the q value update should be used when training with demonstartions
num_demo: Number of episodes in to be used in the demonstration buffer
demo_batch_size: number of samples to be used from the demonstrations buffer, per mpi thread
prm_loss_weight: Weight corresponding to the primary loss
aux_loss_weight: Weight corresponding to the auxilliary loss also called the cloning loss
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None, )
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {
key: (self.T - 1 if key != 'o' else self.T, *input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T, self.dimg)
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T,
self.sample_transitions)
global DEMO_BUFFER
DEMO_BUFFER = ReplayBuffer(
buffer_shapes, buffer_size, self.T, self.sample_transitions
) #initialize the demo buffer; in the same way as the primary data buffer
def _random_action(self, n):
return np.random.uniform(low=-self.max_u,
high=self.max_u,
size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def step(self, obs):
actions = self.get_actions(obs['observation'], obs['achieved_goal'],
obs['desired_goal'])
return actions, None, None, None
def get_actions(self,
o,
ag,
g,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf:
o.reshape(-1, self.dimo),
policy.g_tf:
g.reshape(-1, self.dimg),
policy.u_tf:
np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(
*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (
self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def init_demo_buffer(
self,
demoDataFile,
update_stats=True): #function that initializes the demo buffer
demoData = np.load(
demoDataFile) #load the demonstration data from data file
info_keys = [
key.replace('info_', '') for key in self.input_dims.keys()
if key.startswith('info_')
]
info_values = [
np.empty((self.T - 1, 1, self.input_dims['info_' + key]),
np.float32) for key in info_keys
]
demo_data_obs = demoData['obs']
demo_data_acs = demoData['acs']
demo_data_info = demoData['info']
for epsd in range(
self.num_demo
): # we initialize the whole demo buffer at the start of the training
obs, acts, goals, achieved_goals = [], [], [], []
i = 0
for transition in range(self.T - 1):
obs.append(
[demo_data_obs[epsd][transition].get('observation')])
acts.append([demo_data_acs[epsd][transition]])
goals.append(
[demo_data_obs[epsd][transition].get('desired_goal')])
achieved_goals.append(
[demo_data_obs[epsd][transition].get('achieved_goal')])
for idx, key in enumerate(info_keys):
info_values[idx][transition,
i] = demo_data_info[epsd][transition][key]
obs.append([demo_data_obs[epsd][self.T - 1].get('observation')])
achieved_goals.append(
[demo_data_obs[epsd][self.T - 1].get('achieved_goal')])
episode = dict(o=obs, u=acts, g=goals, ag=achieved_goals)
for key, value in zip(info_keys, info_values):
episode['info_{}'.format(key)] = value
episode = convert_episode_to_batch_major(episode)
global DEMO_BUFFER
DEMO_BUFFER.store_episode(
episode
) # create the observation dict and append them into the demonstration buffer
logger.debug("Demo buffer size currently ",
DEMO_BUFFER.get_current_size()
) #print out the demonstration buffer size
if update_stats:
# add transitions to normalizer to normalize the demo data as well
episode['o_2'] = episode['o'][:, 1:, :]
episode['ag_2'] = episode['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode)
transitions = self.sample_transitions(
episode, num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions[
'ag']
transitions['o'], transitions['g'] = self._preprocess_og(
o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
episode.clear()
logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()
) #print out the demonstration buffer size
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
transitions = self.sample_transitions(episode_batch,
num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
self.Q_loss_tf, self.main.Q_pi_tf, self.Q_grad_tf, self.pi_grad_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
if self.bc_loss: #use demonstration buffer to sample as well if bc_loss flag is set TRUE
transitions = self.buffer.sample(self.batch_size -
self.demo_batch_size)
global DEMO_BUFFER
transitions_demo = DEMO_BUFFER.sample(
self.demo_batch_size) #sample from the demo buffer
for k, values in transitions_demo.items():
rolloutV = transitions[k].tolist()
for v in values:
rolloutV.append(v.tolist())
transitions[k] = np.array(rolloutV)
else:
transitions = self.buffer.sample(
self.batch_size) #otherwise only sample from primary buffer
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(
o_2, ag_2, g)
assert np.array_equal(transitions['g_2'], transitions['g'])
transitions_batch = [
transitions[key] for key in self.stage_shapes.keys()
]
return transitions_batch
def stage_batch(self, batch=None):
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
if stage:
self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
self._update(Q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dimu, self.max_u))
self.sess = tf_util.get_session()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([
(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
#choose only the demo buffer samples
mask = np.concatenate(
(np.zeros(self.batch_size - self.demo_batch_size),
np.ones(self.demo_batch_size)),
axis=0)
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf,
net_type='main',
**self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(target_batch_tf,
net_type='target',
**self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return,
0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(
batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
self.Q_loss_tf = tf.reduce_mean(
tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
if self.bc_loss == 1 and self.q_filter == 1: # train with demonstrations and use bc_loss and q_filter both
maskMain = tf.reshape(
tf.boolean_mask(self.main.Q_tf > self.main.Q_pi_tf,
mask), [-1]
) #where is the demonstrator action better than actor action according to the critic? choose those samples only
#define the cloning loss on the actor's actions only on the samples which adhere to the above masks
self.cloning_loss_tf = tf.reduce_sum(
tf.square(
tf.boolean_mask(tf.boolean_mask((self.main.pi_tf), mask),
maskMain,
axis=0) -
tf.boolean_mask(tf.boolean_mask((batch_tf['u']), mask),
maskMain,
axis=0)))
self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(
self.main.Q_pi_tf
) #primary loss scaled by it's respective weight prm_loss_weight
self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u)
) #L2 loss on action values scaled by the same weight prm_loss_weight
self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf #adding the cloning loss to the actor loss as an auxilliary loss scaled by its weight aux_loss_weight
elif self.bc_loss == 1 and self.q_filter == 0: # train with demonstrations without q_filter
self.cloning_loss_tf = tf.reduce_sum(
tf.square(
tf.boolean_mask((self.main.pi_tf), mask) -
tf.boolean_mask((batch_tf['u']), mask)))
self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(
self.main.Q_pi_tf)
self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf
else: #If not training with demonstrations
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf,
var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf,
var_list=self._vars('main/pi'))
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=False)
# polyak averaging
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars(
'g_stats')
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]),
zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix != '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main',
'target', 'lock', 'env', 'sample_transitions', 'stage_shapes',
'create_actor_critic'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([not subname in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert (len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
def save(self, save_path):
tf_util.save_variables(save_path)
class DDPG(object):
@store_args
def __init__(self,
input_dims,
buffer_size,
hidden,
layers,
network_class,
polyak,
batch_size,
Q_lr,
pi_lr,
norm_eps,
norm_clip,
action_scale,
action_l2,
clip_obs,
scope,
T,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
bc_loss,
q_filter,
num_demo,
demo_batch_size,
prm_loss_weight,
aux_loss_weight,
sample_transitions,
gamma,
temperature,
prioritization,
env_name,
alpha,
beta0,
beta_iters,
total_timesteps,
rank_method,
reuse=False,
**kwargs):
"""
Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Added functionality to use demonstrations for training to Overcome exploration problem.
Args:
:param input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
:param buffer_size (int): number of transitions that are stored in the replay buffer
:param hidden (int): number of units in the hidden layers
:param layers (int): number of hidden layers
:param network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
:param polyak (float): coefficient for Polyak-averaging of the target network
:param batch_size (int): batch size for training
:param Q_lr (float): learning rate for the Q (critic) network
:param pi_lr (float): learning rate for the pi (actor) network
:param norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
:param norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
:param action_scale(float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
:param action_l2 (float): coefficient for L2 penalty on the actions
:param clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
:param scope (str): the scope used for the TensorFlow graph
:param T (int): the time horizon for rollouts
:param rollout_batch_size (int): number of parallel rollouts per DDPG agent
:param subtract_goals (function): function that subtracts goals from each other
:param relative_goals (boolean): whether or not relative goals should be fed into the network
:param clip_pos_returns (boolean): whether or not positive returns should be clipped
:param clip_return (float): clip returns to be in [-clip_return, clip_return]
:param sample_transitions (function) function that samples from the replay buffer
:param gamma (float): gamma used for Q learning updates
:param reuse (boolean): whether or not the networks should be reused
:param bc_loss: whether or not the behavior cloning loss should be used as an auxilliary loss
:param q_filter: whether or not a filter on the q value update should be used when training with demonstartions
:param num_demo: Number of episodes in to be used in the demonstration buffer
:param demo_batch_size: number of samples to be used from the demonstrations buffer, per mpi thread
:param prm_loss_weight: Weight corresponding to the primary loss
:param aux_loss_weight: Weight corresponding to the auxilliary loss also called the cloning loss
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(
self.network_class) # points to actor_critic.py
self.input_dims = input_dims
input_shapes = dims_to_shapes(input_dims)
self.dimo = input_dims['o']
self.dimg = input_dims['g']
self.dimu = input_dims['u']
self.sample_count = 1
self.cycle_count = 1
self.critic_loss_episode = []
self.actor_loss_episode = []
self.critic_loss_avg = []
self.actor_loss_avg = []
# Energy based parameters
self.prioritization = prioritization
self.env_name = env_name
self.temperature = temperature
self.rank_method = rank_method
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None, )
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse) # Creates DDPG agent
# Configure the replay buffer.
buffer_shapes = {
key: (self.T - 1 if key != 'o' else self.T, *input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T, self.dimg)
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
# print("begin init")
if self.prioritization == 'energy':
self.buffer = ReplayBufferEnergy(buffer_shapes, buffer_size,
self.T, self.sample_transitions,
self.prioritization,
self.env_name)
# elif self.prioritization == 'tderror':
# self.buffer = PrioritizedReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions, alpha)
# if beta_iters is None:
# beta_iters = total_timesteps
# self.beta_schedule = LinearSchedule(beta_iters, initial_p=beta0, final_p=1.0)
else:
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T,
self.sample_transitions)
# print("finish init")
def _random_action(self, n):
return np.random.uniform(low=-self.action_scale,
high=self.action_scale,
size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals: # no self.relative_goals
print("self.relative_goals: ", self.relative_goals)
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
# Clip (limit) the values in an array.
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
# Not used
def step(self, obs):
actions = self.get_actions(obs['observation'], obs['achieved_goal'],
obs['desired_goal'])
return actions, None, None, None
def get_actions(self,
o,
ag,
g,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
# Use target network use main network
policy = self.target if use_target_net else self.main
# values to compute
policy_weights = [policy.actor_tf]
if compute_Q:
policy_weights += [policy.critic_with_actor_tf]
# feeds
agent_feed = {
policy.obs:
o.reshape(-1, self.dimo),
policy.goals:
g.reshape(-1, self.dimg),
policy.actions:
np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
# Evaluating policy weights with agent information
ret = self.sess.run(policy_weights, feed_dict=agent_feed)
# print(ret)
# action postprocessing
action = ret[0]
noise = noise_eps * self.action_scale * np.random.randn(
*action.shape) # gaussian noise
action += noise
action = np.clip(action, -self.action_scale, self.action_scale)
action += np.random.binomial(1, random_eps, action.shape[0]).reshape(
-1, 1) * (self._random_action(action.shape[0]) - action
) # eps-greedy
if action.shape[0] == 1:
action = action[0]
action = action.copy()
ret[0] = action
if len(ret) == 1:
return ret[0]
else:
return ret
# Not used
# def init_demo_buffer(self, demoDataFile, update_stats=True): # function that initializes the demo buffer
#
# demoData = np.load(demoDataFile) # load the demonstration data from data file
# info_keys = [key.replace('info_', '') for key in self.input_dims.keys() if key.startswith('info_')]
# info_values = [np.empty((self.T - 1, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys]
#
# demo_data_obs = demoData['obs']
# demo_data_acs = demoData['acs']
# demo_data_info = demoData['info']
#
# for epsd in range(self.num_demo): # we initialize the whole demo buffer at the start of the training
# obs, acts, goals, achieved_goals = [], [], [], []
# i = 0
# for transition in range(self.T - 1):
# obs.append([demo_data_obs[epsd][transition].get('observation')])
# acts.append([demo_data_acs[epsd][transition]])
# goals.append([demo_data_obs[epsd][transition].get('desired_goal')])
# achieved_goals.append([demo_data_obs[epsd][transition].get('achieved_goal')])
# for idx, key in enumerate(info_keys):
# info_values[idx][transition, i] = demo_data_info[epsd][transition][key]
#
# obs.append([demo_data_obs[epsd][self.T - 1].get('observation')])
# achieved_goals.append([demo_data_obs[epsd][self.T - 1].get('achieved_goal')])
#
# episode = dict(observations=obs,
# u=acts,
# g=goals,
# ag=achieved_goals)
# for key, value in zip(info_keys, info_values):
# episode['info_{}'.format(key)] = value
#
# episode = convert_episode_to_batch_major(episode)
# global DEMO_BUFFER
# DEMO_BUFFER.ddpg_store_episode(
# episode) # create the observation dict and append them into the demonstration buffer
# logger.debug("Demo buffer size currently ",
# DEMO_BUFFER.get_current_size()) # print out the demonstration buffer size
#
# if update_stats:
# # add transitions to normalizer to normalize the demo data as well
# episode['o_2'] = episode['o'][:, 1:, :]
# episode['ag_2'] = episode['ag'][:, 1:, :]
# num_normalizing_transitions = transitions_in_episode_batch(episode)
# transitions = self.sample_transitions(episode, num_normalizing_transitions)
#
# o, g, ag = transitions['o'], transitions['g'], transitions['ag']
# transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# # No need to preprocess the o_2 and g_2 since this is only used for stats
#
# self.o_stats.update(transitions['o'])
# self.g_stats.update(transitions['g'])
#
# self.o_stats.recompute_stats()
# self.g_stats.recompute_stats()
# episode.clear()
#
# logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()) # print out the demonstration buffer size
def ddpg_store_episode(self,
episode_batch,
dump_buffer,
w_potential,
w_linear,
w_rotational,
rank_method,
clip_energy,
update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
# if self.prioritization == 'tderror':
# self.buffer.store_episode(episode_batch, dump_buffer)
# print("DDPG BEGIN STORE episode")
if self.prioritization == 'energy':
self.buffer.store_episode(episode_batch, w_potential, w_linear,
w_rotational, rank_method, clip_energy)
else:
self.buffer.store_episode(episode_batch)
# print("DDPG END STORE episode")
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
# print("START ddpg sample transition")
# n_cycles calls HER sampler
if self.prioritization == 'energy':
if not self.buffer.current_size == 0 and not len(
episode_batch['ag']) == 0:
transitions = self.sample_transitions(
episode_batch, num_normalizing_transitions, 'none',
1.0, self.sample_count, self.cycle_count, True)
# elif self.prioritization == 'tderror':
# transitions, weights, episode_idxs = \
# self.sample_transitions(self.buffer, episode_batch, num_normalizing_transitions, beta=0)
else:
transitions = self.sample_transitions(
episode_batch, num_normalizing_transitions)
# print("END ddpg sample transition")
# print("DDPG END STORE episode 2")
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.critic_optimiser.sync()
self.actor_optimiser.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, critic_grad, actor_grad, td_error = self.sess.run(
[
self.critic_loss_tf, # MSE of target_tf - main.critic_tf
self.main.critic_with_actor_tf, # actor_loss
self.critic_grads,
self.actor_grads,
self.td_error_tf
])
return critic_loss, actor_loss, critic_grad, actor_grad, td_error
def _update(self, critic_grads, actor_grads):
self.critic_optimiser.update(critic_grads, self.Q_lr)
self.actor_optimiser.update(actor_grads, self.pi_lr)
def sample_batch(self, t):
# print("Begin Sample batch")
if self.prioritization == 'energy':
transitions = self.buffer.sample(self.batch_size,
self.rank_method,
temperature=self.temperature)
weights = np.ones_like(transitions['r']).copy()
# print("reach?")
# elif self.prioritization == 'tderror':
# transitions, weights, idxs = self.buffer.sample(self.batch_size, beta=self.beta_schedule.value(t))
else:
transitions = self.buffer.sample(self.batch_size)
weights = np.ones_like(transitions['r']).copy()
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(
o_2, ag_2, g)
transitions['w'] = weights.flatten().copy() # note: ordered dict
transitions_batch = [
transitions[key] for key in self.stage_shapes.keys()
]
# if self.prioritization == 'tderror':
# return (transitions_batch, idxs)
# else:
# print("End sample batch")
return transitions_batch
def stage_batch(self, t, batch=None):
if batch is None:
# if self.prioritization == 'tderror':
# batch, idxs = self.sample_batch(t)
# else:
batch = self.sample_batch(t)
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
# if self.prioritization == 'tderror':
# return idxs
def ddpg_train(self, t, dump_buffer, stage=True):
if stage:
# if self.prioritization == 'tderror':
# idxs = self.stage_batch(t)
# else:
self.stage_batch(t)
self.critic_loss, self.actor_loss, Q_grad, pi_grad, td_error = self._grads(
)
# if self.prioritization == 'tderror':
# new_priorities = np.abs(td_error) + self.eps # td_error
# if dump_buffer:
# T = self.buffer.buffers['u'].shape[1]
# episode_idxs = idxs // T
# t_samples = idxs % T
# batch_size = td_error.shape[0]
# with self.buffer.lock:
# for i in range(batch_size):
# self.buffer.buffers['td'][episode_idxs[i]][t_samples[i]] = td_error[i]
#
# self.buffer.update_priorities(idxs, new_priorities)
# Update gradients for actor and critic networks
self._update(Q_grad, pi_grad)
# My variables
self.visual_actor_loss = 1 - self.actor_loss
self.critic_loss_episode.append(self.critic_loss)
self.actor_loss_episode.append(self.visual_actor_loss)
# print("Critic loss: ", self.critic_loss, " Actor loss: ", self.actor_loss)
return self.critic_loss, np.mean(self.actor_loss)
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def ddpg_update_target_net(self):
# print("ddpg_cycle", self.cycle_count)
self.cycle_count += 1
self.critic_loss_avg = np.mean(self.critic_loss_episode)
self.actor_loss_avg = np.mean(self.actor_loss_episode)
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dimu, self.action_scale))
self.sess = tf_util.get_session()
# running averages
with tf.variable_scope('o_stats') as variable_scope:
if reuse:
variable_scope.reuse_variables()
self.o_stats = Normalizer(self.dimo,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats') as variable_scope:
if reuse:
variable_scope.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([
(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# choose only the demo buffer samples
mask = np.concatenate(
(np.zeros(self.batch_size - self.demo_batch_size),
np.ones(self.demo_batch_size)),
axis=0)
# networks
with tf.variable_scope('main') as variable_scope:
if reuse:
variable_scope.reuse_variables()
# Create actor critic network
self.main = self.create_actor_critic(batch_tf,
net_type='main',
**self.__dict__)
variable_scope.reuse_variables()
with tf.variable_scope('target') as variable_scope:
if reuse:
variable_scope.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(target_batch_tf,
net_type='target',
**self.__dict__)
variable_scope.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_critic_actor_tf = self.target.critic_with_actor_tf
clip_range = (-self.clip_return,
0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(
batch_tf['r'] + self.gamma * target_critic_actor_tf, *clip_range)
# MSE of target_tf - critic_tf. This is the TD Learning step
self.td_error_tf = tf.stop_gradient(target_tf) - self.main.critic_tf
self.critic_loss_tf = tf.reduce_mean(
tf.square(tf.stop_gradient(target_tf) - self.main.critic_tf))
#
self.actor_loss_tf = -tf.reduce_mean(self.main.critic_with_actor_tf)
self.actor_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.actor_tf / self.action_scale))
# Constructs symbolic derivatives of sum of critic_loss_tf vs _vars('main/Q')
critic_grads_tf = tf.gradients(self.critic_loss_tf,
self._vars('main/Q'))
actor_grads_tf = tf.gradients(self.actor_loss_tf,
self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(critic_grads_tf)
assert len(self._vars('main/pi')) == len(actor_grads_tf)
self.critic_grads_vars_tf = zip(critic_grads_tf, self._vars('main/Q'))
self.actor_grads_vars_tf = zip(actor_grads_tf, self._vars('main/pi'))
# Flattens variables and their gradients.
self.critic_grads = flatten_grads(grads=critic_grads_tf,
var_list=self._vars('main/Q'))
self.actor_grads = flatten_grads(grads=actor_grads_tf,
var_list=self._vars('main/pi'))
# optimizers
self.critic_optimiser = MpiAdam(self._vars('main/Q'),
scale_grad_by_procs=False)
self.actor_optimiser = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=False)
# polyak averaging used to update target network
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars(
'g_stats')
# list( map( lambda( assign() ), zip()))
self.init_target_net_op = list(
map( # Apply lambda to each item item in the zipped list
lambda v: v[0].assign(v[1]),
zip(self.target_vars, self.main_vars)))
# Polyak-Ruppert averaging where most recent iterations are weighted more than past iterations.
self.update_target_net_op = list(
map( # Apply lambda to each item item in the zipped list
lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) *
v[1]), # polyak averaging
zip(self.target_vars,
self.main_vars)) # [(target_vars, main_vars), (), ...]
)
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('actor_critic/critic_loss', self.critic_loss_avg)]
logs += [('actor_critic/actor_loss', self.actor_loss_avg)]
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
# logs += [('critic_loss', np.mean(self.sess.run([self.critic_loss])))]
# logs += [('actor_loss', np.mean(self.sess.run([self.actor_loss])))]
if prefix != '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main',
'target', 'lock', 'env', 'sample_transitions', 'stage_shapes',
'create_actor_critic'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([not subname in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert (len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
def save(self, save_path):
tf_util.save_variables(save_path)
class DDPG(object):
def __init__(self,
input_dims,
buffer_size,
hidden,
layers,
network_class,
polyak,
batch_size,
q_lr,
pi_lr,
norm_eps,
norm_clip,
max_u,
action_l2,
clip_obs,
scope,
time_horizon,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
sample_transitions,
gamma,
reuse=False):
"""
Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
:param input_dims: ({str: int}) dimensions for the observation (o), the goal (g), and the actions (u)
:param buffer_size: (int) number of transitions that are stored in the replay buffer
:param hidden: (int) number of units in the hidden layers
:param layers: (int) number of hidden layers
:param network_class: (str) the network class that should be used (e.g. 'baselines.her.ActorCritic')
:param polyak: (float) coefficient for Polyak-averaging of the target network
:param batch_size: (int) batch size for training
:param q_lr: (float) learning rate for the Q (critic) network
:param pi_lr: (float) learning rate for the pi (actor) network
:param norm_eps: (float) a small value used in the normalizer to avoid numerical instabilities
:param norm_clip: (float) normalized inputs are clipped to be in [-norm_clip, norm_clip]
:param max_u: (float) maximum action magnitude, i.e. actions are in [-max_u, max_u]
:param action_l2: (float) coefficient for L2 penalty on the actions
:param clip_obs: (float) clip observations before normalization to be in [-clip_obs, clip_obs]
:param scope: (str) the scope used for the TensorFlow graph
:param time_horizon: (int) the time horizon for rollouts
:param rollout_batch_size: (int) number of parallel rollouts per DDPG agent
:param subtract_goals: (function (numpy Number, numpy Number): numpy Number) function that subtracts goals
from each other
:param relative_goals: (boolean) whether or not relative goals should be fed into the network
:param clip_pos_returns: (boolean) whether or not positive returns should be clipped
:param clip_return: (float) clip returns to be in [-clip_return, clip_return]
:param sample_transitions: (function (dict, int): dict) function that samples from the replay buffer
:param gamma: (float) gamma used for Q learning updates
:param reuse: (boolean) whether or not the networks should be reused
"""
# Updated in experiments/config.py
self.input_dims = input_dims
self.buffer_size = buffer_size
self.hidden = hidden
self.layers = layers
self.network_class = network_class
self.polyak = polyak
self.batch_size = batch_size
self.q_lr = q_lr
self.pi_lr = pi_lr
self.norm_eps = norm_eps
self.norm_clip = norm_clip
self.max_u = max_u
self.action_l2 = action_l2
self.clip_obs = clip_obs
self.scope = scope
self.time_horizon = time_horizon
self.rollout_batch_size = rollout_batch_size
self.subtract_goals = subtract_goals
self.relative_goals = relative_goals
self.clip_pos_returns = clip_pos_returns
self.clip_return = clip_return
self.sample_transitions = sample_transitions
self.gamma = gamma
self.reuse = reuse
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dim_obs = self.input_dims['o']
self.dim_goal = self.input_dims['g']
self.dim_action = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None, )
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {
key: (self.time_horizon if key != 'o' else self.time_horizon + 1,
*input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dim_goal)
buffer_shapes['ag'] = (self.time_horizon + 1, self.dim_goal)
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size,
self.time_horizon, self.sample_transitions)
def _random_action(self, num):
return np.random.uniform(low=-self.max_u,
high=self.max_u,
size=(num, self.dim_action))
def _preprocess_obs_goal(self, obs, achieved_goal, goal):
if self.relative_goals:
g_shape = goal.shape
goal = goal.reshape(-1, self.dim_goal)
achieved_goal = achieved_goal.reshape(-1, self.dim_goal)
goal = self.subtract_goals(goal, achieved_goal)
goal = goal.reshape(*g_shape)
obs = np.clip(obs, -self.clip_obs, self.clip_obs)
goal = np.clip(goal, -self.clip_obs, self.clip_obs)
return obs, goal
def get_actions(self,
obs,
achieved_goal,
goal,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_q=False):
"""
return the action from an observation and goal
:param obs: (numpy Number) the observation
:param achieved_goal: (numpy Number) the achieved goal
:param goal: (numpy Number) the goal
:param noise_eps: (float) the noise epsilon
:param random_eps: (float) the random epsilon
:param use_target_net: (bool) whether or not to use the target network
:param compute_q: (bool) whether or not to compute Q value
:return: (numpy float or float) the actions
"""
obs, goal = self._preprocess_obs_goal(obs, achieved_goal, goal)
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_q:
vals += [policy.q_pi_tf]
# feed
feed = {
policy.o_tf:
obs.reshape(-1, self.dim_obs),
policy.g_tf:
goal.reshape(-1, self.dim_goal),
policy.u_tf:
np.zeros((obs.size // self.dim_obs, self.dim_action),
dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
action = ret[0]
noise = noise_eps * self.max_u * np.random.randn(
*action.shape) # gaussian noise
action += noise
action = np.clip(action, -self.max_u, self.max_u)
# eps-greedy
n_ac = action.shape[0]
action += np.random.binomial(1, random_eps, n_ac).reshape(
-1, 1) * (self._random_action(n_ac) - action)
if action.shape[0] == 1:
action = action[0]
action = action.copy()
ret[0] = action
if len(ret) == 1:
return ret[0]
else:
return ret
def store_episode(self, episode_batch, update_stats=True):
"""
Story the episode transitions
:param episode_batch: (numpy Number) array of batch_size x (T or T+1) x dim_key 'o' is of size T+1,
others are of size T
:param update_stats: (bool) whether to update stats or not
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
transitions = self.sample_transitions(episode_batch,
num_normalizing_transitions)
obs, _, goal, achieved_goal = transitions['o'], transitions[
'o_2'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_obs_goal(
obs, achieved_goal, goal)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
"""
returns the current buffer size
:return: (int) buffer size
"""
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, q_grad, pi_grad = self.sess.run([
self.q_loss_tf, self.main.q_pi_tf, self.q_grad_tf, self.pi_grad_tf
])
return critic_loss, actor_loss, q_grad, pi_grad
def _update(self, q_grad, pi_grad):
self.q_adam.update(q_grad, self.q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
"""
sample a batch
:return: (dict) the batch
"""
transitions = self.buffer.sample(self.batch_size)
obs, obs_2, goal = transitions['o'], transitions['o_2'], transitions[
'g']
achieved_goal, achieved_goal_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_obs_goal(
obs, achieved_goal, goal)
transitions['o_2'], transitions['g_2'] = self._preprocess_obs_goal(
obs_2, achieved_goal_2, goal)
transitions_batch = [
transitions[key] for key in self.stage_shapes.keys()
]
return transitions_batch
def stage_batch(self, batch=None):
"""
apply a batch to staging
:param batch: (dict) the batch to add to staging, if None: self.sample_batch()
"""
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
"""
train DDPG
:param stage: (bool) enable staging
:return: (float, float) critic loss, actor loss
"""
if stage:
self.stage_batch()
critic_loss, actor_loss, q_grad, pi_grad = self._grads()
self._update(q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
"""
update the target network
"""
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
"""
clears the replay buffer
"""
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dim_action, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as scope:
if reuse:
scope.reuse_variables()
self.o_stats = Normalizer(self.dim_obs,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats') as scope:
if reuse:
scope.reuse_variables()
self.g_stats = Normalizer(self.dim_goal,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([
(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# networks
with tf.variable_scope('main') as scope:
if reuse:
scope.reuse_variables()
self.main = self.create_actor_critic(batch_tf,
net_type='main',
**self.__dict__)
scope.reuse_variables()
with tf.variable_scope('target') as scope:
if reuse:
scope.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(target_batch_tf,
net_type='target',
**self.__dict__)
scope.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_q_pi_tf = self.target.q_pi_tf
clip_range = (-self.clip_return,
0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(
batch_tf['r'] + self.gamma * target_q_pi_tf, *clip_range)
self.q_loss_tf = tf.reduce_mean(
tf.square(tf.stop_gradient(target_tf) - self.main.q_tf))
self.pi_loss_tf = -tf.reduce_mean(self.main.q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
q_grads_tf = tf.gradients(self.q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.q_grads_vars_tf = zip(q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.q_grad_tf = flatten_grads(grads=q_grads_tf,
var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf,
var_list=self._vars('main/pi'))
# optimizers
self.q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=False)
# polyak averaging
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars(
'g_stats')
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]),
zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
"""
create a log dictionary
:param prefix: (str) the prefix for evey index
:return: ({str: Any}) the log
"""
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main',
'target', 'lock', 'env', 'sample_transitions', 'stage_shapes',
'create_actor_critic'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([subname not in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for key, value in state.items():
if key[-6:] == '_stats':
self.__dict__[key] = value
# load TF variables
_vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert len(_vars) == len(state["tf"])
node = [tf.assign(var, val) for var, val in zip(_vars, state["tf"])]
self.sess.run(node)
class DDPG(object):
@store_args
def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size,
Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T,
rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return,
bc_loss, q_filter, num_demo, demo_batch_size, prm_loss_weight, aux_loss_weight,
sample_transitions, gamma, reuse=False, **kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Added functionality to use demonstrations for training to Overcome exploration problem.
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
bc_loss: whether or not the behavior cloning loss should be used as an auxilliary loss
q_filter: whether or not a filter on the q value update should be used when training with demonstartions
num_demo: Number of episodes in to be used in the demonstration buffer
demo_batch_size: number of samples to be used from the demonstrations buffer, per mpi thread
prm_loss_weight: Weight corresponding to the primary loss
aux_loss_weight: Weight corresponding to the auxilliary loss also called the cloning loss
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None,)
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {key: (self.T-1 if key != 'o' else self.T, *input_shapes[key])
for key, val in input_shapes.items()}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T, self.dimg)
buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
global DEMO_BUFFER
DEMO_BUFFER = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) #initialize the demo buffer; in the same way as the primary data buffer
def _random_action(self, n):
return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def step(self, obs):
actions = self.get_actions(obs['observation'], obs['achieved_goal'], obs['desired_goal'])
return actions, None, None, None
def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf: o.reshape(-1, self.dimo),
policy.g_tf: g.reshape(-1, self.dimg),
policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def init_demo_buffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer
demoData = np.load(demoDataFile) #load the demonstration data from data file
info_keys = [key.replace('info_', '') for key in self.input_dims.keys() if key.startswith('info_')]
info_values = [np.empty((self.T - 1, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys]
demo_data_obs = demoData['obs']
demo_data_acs = demoData['acs']
demo_data_info = demoData['info']
for epsd in range(self.num_demo): # we initialize the whole demo buffer at the start of the training
obs, acts, goals, achieved_goals = [], [] ,[] ,[]
i = 0
for transition in range(self.T - 1):
obs.append([demo_data_obs[epsd][transition].get('observation')])
acts.append([demo_data_acs[epsd][transition]])
goals.append([demo_data_obs[epsd][transition].get('desired_goal')])
achieved_goals.append([demo_data_obs[epsd][transition].get('achieved_goal')])
for idx, key in enumerate(info_keys):
info_values[idx][transition, i] = demo_data_info[epsd][transition][key]
obs.append([demo_data_obs[epsd][self.T - 1].get('observation')])
achieved_goals.append([demo_data_obs[epsd][self.T - 1].get('achieved_goal')])
episode = dict(o=obs,
u=acts,
g=goals,
ag=achieved_goals)
for key, value in zip(info_keys, info_values):
episode['info_{}'.format(key)] = value
episode = convert_episode_to_batch_major(episode)
global DEMO_BUFFER
DEMO_BUFFER.store_episode(episode) # create the observation dict and append them into the demonstration buffer
logger.debug("Demo buffer size currently ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
if update_stats:
# add transitions to normalizer to normalize the demo data as well
episode['o_2'] = episode['o'][:, 1:, :]
episode['ag_2'] = episode['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode)
transitions = self.sample_transitions(episode, num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
episode.clear()
logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
self.Q_loss_tf,
self.main.Q_pi_tf,
self.Q_grad_tf,
self.pi_grad_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
if self.bc_loss: #use demonstration buffer to sample as well if bc_loss flag is set TRUE
transitions = self.buffer.sample(self.batch_size - self.demo_batch_size)
global DEMO_BUFFER
transitions_demo = DEMO_BUFFER.sample(self.demo_batch_size) #sample from the demo buffer
for k, values in transitions_demo.items():
rolloutV = transitions[k].tolist()
for v in values:
rolloutV.append(v.tolist())
transitions[k] = np.array(rolloutV)
else:
transitions = self.buffer.sample(self.batch_size) #otherwise only sample from primary buffer
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g)
transitions_batch = [transitions[key] for key in self.stage_shapes.keys()]
return transitions_batch
def stage_batch(self, batch=None):
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
if stage:
self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
self._update(Q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
self.sess = tf_util.get_session()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
#choose only the demo buffer samples
mask = np.concatenate((np.zeros(self.batch_size - self.demo_batch_size), np.ones(self.demo_batch_size)), axis = 0)
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(
target_batch_tf, net_type='target', **self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
if self.bc_loss ==1 and self.q_filter == 1 : # train with demonstrations and use bc_loss and q_filter both
maskMain = tf.reshape(tf.boolean_mask(self.main.Q_tf > self.main.Q_pi_tf, mask), [-1]) #where is the demonstrator action better than actor action according to the critic? choose those samples only
#define the cloning loss on the actor's actions only on the samples which adhere to the above masks
self.cloning_loss_tf = tf.reduce_sum(tf.square(tf.boolean_mask(tf.boolean_mask((self.main.pi_tf), mask), maskMain, axis=0) - tf.boolean_mask(tf.boolean_mask((batch_tf['u']), mask), maskMain, axis=0)))
self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(self.main.Q_pi_tf) #primary loss scaled by it's respective weight prm_loss_weight
self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u)) #L2 loss on action values scaled by the same weight prm_loss_weight
self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf #adding the cloning loss to the actor loss as an auxilliary loss scaled by its weight aux_loss_weight
elif self.bc_loss == 1 and self.q_filter == 0: # train with demonstrations without q_filter
self.cloning_loss_tf = tf.reduce_sum(tf.square(tf.boolean_mask((self.main.pi_tf), mask) - tf.boolean_mask((batch_tf['u']), mask)))
self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf
else: #If not training with demonstrations
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False)
# polyak averaging
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats')
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix != '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
'main', 'target', 'lock', 'env', 'sample_transitions',
'stage_shapes', 'create_actor_critic']
state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert(len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
def save(self, save_path):
tf_util.save_variables(save_path)
class DDPG(object):
@store_args
def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size,
Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T,
rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return,
sample_transitions, gamma, replay_k, reward_fun=None, reuse=False, **kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
if self.clip_return is None:
self.clip_return = np.inf
# Create the actor critic networks. network_class is defined in actor_critic.py
# This class is assigned to network_class when DDPG objest is created
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
# Next state (o_2) and goal at next state (g_2)
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None,)
self.stage_shapes = stage_shapes
# Adding variable for correcting bias - Ameet
self.stage_shapes_new = OrderedDict()
self.stage_shapes_new['bias'] = (None,)
##############################################
# Create network
# Staging area is a datatype in tf to input data into GPUs
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
# Adding bias term from section 3.4 - Ameet
self.staging_tf_new = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes_new.keys()],
shapes=list(self.stage_shapes_new.values()))
self.buffer_ph_tf_new = [
tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes_new.values()]
self.stage_op_new = self.staging_tf_new.put(self.buffer_ph_tf_new)
############################################
self._create_network(reuse=reuse)
# Configure the replay buffer
buffer_shapes = {key: (self.T if key != 'o' else self.T+1, *input_shapes[key])
for key, val in input_shapes.items()}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T+1, self.dimg)
buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
# conf represents the parameters required for initializing the priority_queue
# Remember: The bias gets annealed only conf.total_steps number of times
conf = {'size': self.buffer_size,
'learn_start': self.batch_size,
'batch_size': self.batch_size,
# Using some heuristic to set the partition_num as it matters only when the buffer is not full (unlikely)
'partition_size': (self.replay_k)*100}
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions, conf, self.replay_k)
# global_steps represents the number of batches used for updates
self.global_step = 0
self.debug = {}
def _random_action(self, n):
return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
# Preprocessing by clipping the goal and state variables
# Not sure about the relative_goal part
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
# target is the target policy network and main is the one which is updated
# target is updated by moving the parameters towards that of the main
# pi_tf is the output of the policy network, Q_pi_tf is the output of the Q network used for training pi_tf
# i.e., Q_pi_tf uses the pi_tf's action to evaluate the value
# While just Q_tf uses the action which was actually taken
def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf: o.reshape(-1, self.dimo),
policy.g_tf: g.reshape(-1, self.dimg),
policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
###### Remove the l value - Supposed to be a list of length 2
# First entry consists of transitions with actual goals and second is alternate goals
self.buffer.store_episode(episode_batch)
# ###### Debug
# # This functions was used to check the hypothesis that if TD error is high
# # for a state with some goal, it is high for that states with all other goals
# self.debug_td_error_alternate_actual(debug_transitions)
# Updating stats
## Change this--------------
update_stats = False
###--------------------------
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
# This function is purely for Debugging purposes
def debug_td_error_alternate_actual(self, debug_transitions):
actual_transitions, alternate_transitions = debug_transitions[0], debug_transitions[1]
actual_transitions, alternate_transitions = self.td_error_convert_to_format(actual_transitions),\
self.td_error_convert_to_format(alternate_transitions)
# Calculated priorities
priorities = []
priorities.append(self.get_priorities(actual_transitions))
priorities.append(self.get_priorities(alternate_transitions))
f = open('act_alt_goals.txt', 'a')
# Length of priorities[0] is 100 and priorities[1] is 400
for i in range(len(priorities[0])):
f.write(str(priorities[0][i])+" : ")
for k in range(4):
f.write(str(priorities[1][i*self.replay_k+k])+" : ")
f.write('\n')
f.write("Done Storing One Rollout\n\n\n")
# f.write('The number of transitions are: '+str(len(priorities[0]))+" :: "+str(len(priorities[1]))+"\n")
# This function is purely for Debugging purposes
def td_error_convert_to_format(self, sample_transitions):
# sample_transitions is now a list of transitions, convert it to the usual {key: batch X dim_key}
keys = sample_transitions[0].keys()
# print("Keys in _sample_her_transitions are: "+str(keys))
transitions = {}
for key in keys:
# Initialize for all the keys
transitions[key] = []
# Add transitions one by one to the list
for single_transition in range(len(sample_transitions)):
transitions[key].append(sample_transitions[single_transition][key])
transitions[key] = np.array(transitions[key])
# Reconstruct info dictionary for reward computation.
info = {}
for key, value in transitions.items():
if key.startswith('info_'):
info[key.replace('info_', '')] = value
# print("The keys in transitions are: "+str(transitions.keys()))
reward_params = {k: transitions[k] for k in ['ag_2', 'g']}
reward_params['info'] = info
transitions['r'] = self.reward_fun(**reward_params)
# transitions = {k: transitions[k].reshape(batch_size, *transitions[k].shape[1:])
# for k in transitions.keys()}
return transitions
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
self.Q_loss_tf,
self.main.Q_pi_tf,
self.Q_grad_tf,
self.pi_grad_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad
# Adam update for Q and pi networks
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
# Sample a batch for mini batch gradient descent, already defined in replay_buffer.py
def sample_batch(self):
# Increment the global step
self.global_step += 1
transitions, w, rank_e_id = self.buffer.sample(self.batch_size, self.global_step, self.uniform_priority)
priorities = self.get_priorities(transitions)
# ##### Debug function
# self.debug_td_error(transitions, priorities)
# #####
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g)
# # Remove
# print("Stage Shape keys in sample_batch are: "+str(self.stage_shapes.keys()))
transitions_batch = [transitions[key] for key in self.stage_shapes.keys()]
# Updates the priorities of the sampled transitions in the priority queue
self.buffer.update_priority(rank_e_id, priorities)
return transitions_batch, [w]
# This function is purely for debugging purposes
def debug_td_error(self, transitions, priorities):
f = open('td_error_debug.txt', 'a')
self.debug['actual_goals'] = 0
self.debug['alternate_goals'] = 0
trans = transitions['is_actual_goal']
for t in range(trans.shape[0]):
if trans[t]:
self.debug['actual_goals'] += 1
# f.write('Actual goal transition: '+str(priorities[t])+'\n')
else:
self.debug['alternate_goals'] += 1
# f.write('Alternate goal transition: '+str(priorities[t])+'\n')
f.write('Ratio is: '+str(float(self.debug['alternate_goals'])/self.debug['actual_goals'])+'\n')
del transitions['is_actual_goal']
###### Debug End
def get_priorities(self, transitions):
pi_target = self.target.pi_tf
Q_pi_target = self.target.Q_pi_tf
Q_main = self.main.Q_tf
o = transitions['o']
o_2 = transitions['o_2']
u = transitions['u']
g = transitions['g']
r = transitions['r']
# Check this with Srikanth
ag = transitions['ag']
priorities = np.zeros(o.shape[0])
# file_obj = open("priorities_print","a")
for i in range(o.shape[0]):
o_2_i = np.clip(o_2[i], -self.clip_obs, self.clip_obs)
o_i, g_i = self._preprocess_og(o[i], ag[i], g[i])
u_i = u[i]
# Not sure about the o_2_i.size // self.dimo. I guess we need not pass one at a time
feed_target = {
self.target.o_tf: o_2_i.reshape(-1, self.dimo),
self.target.g_tf: g_i.reshape(-1, self.dimg),
self.target.u_tf: np.zeros((o_2_i.size // self.dimo, self.dimu), dtype=np.float32)
}
# u_tf for main network is just the action taken at that state
feed_main = {
self.main.o_tf: o_i.reshape(-1, self.dimo),
self.main.g_tf: g_i.reshape(-1, self.dimg),
self.main.u_tf: u_i.reshape(-1, self.dimu)
}
TD = r[i] + self.gamma*self.sess.run(Q_pi_target, feed_dict=feed_target) - self.sess.run(Q_main, feed_dict=feed_main)
priorities[i] = abs(TD)
text = str(TD)
# file_obj.write(text)
# file_obj.close()
return priorities
def stage_batch(self, batch=None):
if batch is None:
batch, bias = self.sample_batch()
# print("Batch type is: "+str(type(batch)))
# print("Batch Shape is: "+str(len(batch)))
# print(str(type(batch[0])))
assert len(self.buffer_ph_tf) == len(batch), "Expected: "+str(len(self.buffer_ph_tf))+" Got: "+str(len(batch))
self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
##### Adding for bias - Ameet
assert len(self.buffer_ph_tf_new) == len(bias), "Expected: "+str(len(self.buffer_ph_tf_new))+" Got: "+str(len(bias))
self.sess.run(self.stage_op_new, feed_dict=dict(zip(self.buffer_ph_tf_new, bias)))
#####
# print("Completed stage batch")
def train(self, stage=True):
if stage:
self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
# print("In ddpg priority:: The shapes of Q_grad and pi_grad are: "+str(Q_grad.shape)+"::"+str(pi_grad.shape))
# print("Their types are::"+str(type(Q_grad)))
self._update(Q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
########### Getting the bias terms - Ameet
bias = self.staging_tf_new.get()
bias_tf = OrderedDict([(key, bias[i])
for i, key in enumerate(self.stage_shapes_new.keys())])
bias_tf['bias'] = tf.reshape(bias_tf['bias'], [-1, 1])
#######################################
# Create main and target networks, each will have a pi_tf, Q_tf and Q_pi_tf
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(
target_batch_tf, net_type='target', **self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
############## Added for bias - Ameet
error = (tf.stop_gradient(target_tf) - self.main.Q_tf) * bias_tf['bias']
self.Q_loss_tf = tf.reduce_mean(tf.square(error))
# self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf * bias_tf['bias'])
# Note that the following statement does not include bias because of the remark in the IEEE paper
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
##############
# Regularization - L2 - Check - Penalty for taking the best action
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
################### Shape Info
####Shape of Q_grads_tf is: 8
####Shape of Q_grads_tf[0] is: (17, 256)
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False)
# polyak averaging
# 'main/Q' is a way of communicating the scope of the variables
# _vars has a way to understand this
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats')
# Update the networks
# target net is updated by using polyak averaging
# target net is initialized by just copying the main net
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
'main', 'target', 'lock', 'env', 'sample_transitions',
'stage_shapes', 'create_actor_critic']
state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert(len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
class ValueEnsemble:
@store_args
def __init__(self,
*,
input_dims,
size_ensemble,
use_Q,
use_double_network,
buffer_size,
hidden,
layers,
batch_size,
lr,
norm_eps,
norm_clip,
polyak,
max_u,
clip_obs,
scope,
T,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
sample_transitions,
gamma,
reuse=False,
**kwargs):
"""Implementation of value function ensemble.
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
size_ensemble (int): number of value functions in the ensemble
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
batch_size (int): batch size for training
lr (float): learning rate for the Q (critic) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped in Bellman update
inference_clip_pos_returns (boolean): whether or not output of the value output used for disagreement should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
if self.use_double_network:
self.use_Q = True
self.create_v_function = DoubleQFunction
elif self.use_Q:
self.create_v_function = QFunction
else:
self.create_v_function = VFunction
if self.clip_return is None:
self.clip_return = np.inf
# self.inference_clip_range = (-self.clip_return, 0. if inference_clip_pos_returns else self.clip_return)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
if self.use_Q:
stage_shapes['u_2'] = stage_shapes['u']
stage_shapes['r'] = (None, )
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = [None] * self.size_ensemble
self.stage_ops = [None] * self.size_ensemble
self.buffer_ph_tf = []
for e in range(self.size_ensemble):
staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
stage_op = staging_tf.put(buffer_ph_tf)
# store in attribute list
self.staging_tf[e] = staging_tf
self.buffer_ph_tf.extend(buffer_ph_tf)
self.stage_ops[e] = stage_op
if self.use_double_network:
self._create_double_network(reuse=reuse)
else:
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {
key: (self.T - 1 if key != 'o' else self.T, *input_shapes[key])
for key, val in input_shapes.items()
}
buffer_shapes['ag'] = (self.T, self.dimg)
# if self.use_Q:
# buffer_shapes['u_2'] = (self.T-1, self.dimu)
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T,
self.sample_transitions)
# @property
# def buffer_full(self):
# return self.buffer.full
# def buffer_get_transitions_stored(self):
# return self.buffer.get_transitions_stored()
def get_values(self, o, ag, g, u=None):
if self.size_ensemble == 0:
return None
if u is not None:
assert self.use_Q
u = self._preprocess_u(u)
o, g = self._preprocess_og(o, ag, g)
# values to compute
vars = [v_function.V_tf for v_function in self.V_fun]
# feed
feed = {}
for e in range(self.size_ensemble):
feed[self.V_fun[e].o_tf] = o.reshape(-1, self.dimo)
feed[self.V_fun[e].g_tf] = g.reshape(-1, self.dimg)
if self.use_Q:
feed[self.V_fun[e].u_tf] = u.reshape(-1, self.dimu)
ret = self.sess.run(vars, feed_dict=feed)
# value prediction postprocessing
# ret = np.clip(ret, -self.clip_return, 0. if self.clip_pos_returns else self.clip_return)
ret = np.clip(ret, -self.clip_return,
0. if self.clip_pos_returns else np.inf)
return ret
def _sample_batch(self, policy):
batch_size_in_transitions = self.batch_size * self.size_ensemble
transitions = self.buffer.sample(batch_size_in_transitions)
# label policy
if self.use_Q:
u = transitions['u']
u_2 = policy.get_actions(o=transitions['o_2'],
ag=transitions['ag_2'],
g=transitions['g'])
transitions['u'] = self._preprocess_u(u)
transitions['u_2'] = self._preprocess_u(u_2)
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(
o_2, ag_2, g)
transitions_batches = [
transitions[key][e * self.batch_size:(e + 1) * self.batch_size]
for e in range(self.size_ensemble)
for key in self.stage_shapes.keys()
]
return transitions_batches
def _stage_batch(self, policy):
batches = self._sample_batch(policy=policy)
assert len(self.buffer_ph_tf) == len(batches)
self.sess.run(self.stage_ops,
feed_dict=dict(zip(self.buffer_ph_tf, batches)))
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix != '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def train(self, policy):
self._stage_batch(policy=policy)
V_loss, V_grad = self._grads()
self._update(V_grad)
assert len(V_loss) == self.size_ensemble
return np.mean(V_loss)
def _update(self, V_grad):
for e in range(self.size_ensemble):
self.V_adam[e].update(V_grad[e], self.lr)
def _create_network(self, reuse=False):
# logger.info("Creating a q function ensemble with action space %d x %s..." % (self.dimu, self.max_u))
# self.sess = tf_util.get_session()
self.sess = tf.get_default_session()
assert self.sess is not None
# running averages, separate from alg (this is within a different scope)
# assume reuse is False
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats'):
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
self.V_loss_tf = [None] * self.size_ensemble
self.V_fun = [None] * self.size_ensemble
self.V_grads_vars_tf = [None] * self.size_ensemble
self.V_grad_tf = [None] * self.size_ensemble
self.V_adam = [None] * self.size_ensemble
clip_range = (-self.clip_return,
0. if self.clip_pos_returns else self.clip_return)
for e in range(self.size_ensemble):
# mini-batch sampling
batch = self.staging_tf[e].get()
batch_tf = OrderedDict([
(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# networks (no target network for now)
with tf.variable_scope("ve_{}".format(e)) as vs:
if reuse:
vs.reuse_variables()
v_function = self.create_v_function(batch_tf, **self.__dict__)
vs.reuse_variables()
# loss functions
V_2_tf = v_function.V_2_tf
target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * V_2_tf,
*clip_range)
V_loss_tf = tf.reduce_mean(
tf.square(tf.stop_gradient(target_tf) - v_function.V_tf))
V_scope = 've_{}/V'.format(e)
V_grads_tf = tf.gradients(V_loss_tf, self._vars(V_scope))
assert len(self._vars(V_scope)) == len(V_grads_tf)
V_grads_vars_tf = zip(V_grads_tf, self._vars(V_scope))
V_grad_tf = flatten_grads(grads=V_grads_tf,
var_list=self._vars(V_scope))
# optimizers
V_adam = MpiAdam(self._vars(V_scope), scale_grad_by_procs=False)
# store in attribute lists
self.V_loss_tf[e] = V_loss_tf
self.V_fun[e] = v_function
self.V_grads_vars_tf[e] = V_grads_vars_tf
self.V_grad_tf[e] = V_grad_tf
self.V_adam[e] = V_adam
n_vars = [
len(self._vars("ve_{}".format(e)))
for e in range(self.size_ensemble)
]
assert np.all(np.asarray(n_vars) == n_vars[0]), n_vars
# report loss as the average of value function loss over the ensemble
# self.V_loss_tf = tf.reduce_mean(self.V_loss_tf)
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
def _create_double_network(self, reuse=False):
# logger.info("Creating a q function ensemble with action space %d x %s..." % (self.dimu, self.max_u))
self.sess = tf_util.get_session()
# running averages, separate from alg (this is within a different scope)
# assume reuse is False
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('g_stats'):
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
self.V_loss_tf = [None] * self.size_ensemble
self.V_fun = [None] * self.size_ensemble
self.V_target_fun = [None] * self.size_ensemble
self.V_grads_vars_tf = [None] * self.size_ensemble
self.V_grad_tf = [None] * self.size_ensemble
self.V_adam = [None] * self.size_ensemble
self.init_target_net_op = [None] * self.size_ensemble
self.update_target_net_op = [None] * self.size_ensemble
clip_range = (-self.clip_return,
0. if self.clip_pos_returns else self.clip_return)
for e in range(self.size_ensemble):
# mini-batch sampling
batch = self.staging_tf[e].get()
batch_tf = OrderedDict([
(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# networks (no target network for now)
with tf.variable_scope(f've_{e}') as vs:
if reuse:
vs.reuse_variables()
v_function = self.create_v_function(batch_tf, **self.__dict__)
vs.reuse_variables()
with tf.variable_scope(f've_{e}_target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
target_batch_tf['u'] = batch_tf['u_2']
v_target_function = self.create_v_function(
target_batch_tf, **self.__dict__)
vs.reuse_variables()
# loss functions
target_tf = tf.clip_by_value(
batch_tf['r'] + self.gamma * v_target_function.V_tf,
*clip_range)
V_loss_tf = tf.reduce_mean(
tf.square(tf.stop_gradient(target_tf) - v_function.V_tf))
V_scope = f've_{e}/V'
V_grads_tf = tf.gradients(V_loss_tf, self._vars(V_scope))
assert len(self._vars(V_scope)) == len(V_grads_tf)
V_grads_vars_tf = zip(V_grads_tf, self._vars(V_scope))
V_grad_tf = flatten_grads(grads=V_grads_tf,
var_list=self._vars(V_scope))
# optimizers
V_adam = MpiAdam(self._vars(V_scope), scale_grad_by_procs=False)
# store in attribute lists
self.V_loss_tf[e] = V_loss_tf
self.V_fun[e] = v_function
self.V_target_fun[e] = v_target_function
self.V_grads_vars_tf[e] = V_grads_vars_tf
self.V_grad_tf[e] = V_grad_tf
self.V_adam[e] = V_adam
# polyak averaging
main_vars = sum(
[self._vars(f've_{e}/V') for e in range(self.size_ensemble)], [])
target_vars = sum([
self._vars(f've_{e}_target/V') for e in range(self.size_ensemble)
], [])
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(target_vars, main_vars)))
self.update_target_net_op = list(
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(target_vars, main_vars)))
assert len(main_vars) == len(target_vars)
# report loss as the average of value function loss over the ensemble
# self.V_loss_tf = tf.reduce_mean(self.V_loss_tf)
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
if self.use_double_network:
self.sess.run(self.update_target_net_op)
else:
pass
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _sync_optimizers(self):
for e in range(self.size_ensemble):
self.V_adam[e].sync()
def _grads(self):
"""
returns:
V_loss (scalar)
V_grad (list)
"""
V_loss, V_grad = self.sess.run([
self.V_loss_tf,
self.V_grad_tf,
])
return V_loss, V_grad
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
# if self.use_Q:
# u_2 = policy.get_actions(o=episode_batch['o'][:, 1:, :], ag=episode_batch['ag'][:, 1:, :], g=episode_batch['g']) # (batch_size x t x dimu)
# self.buffer.store_episode({**episode_batch, 'u_2': u_2.reshape(episode_batch['u'].shape)})
# else:
# self.buffer.store_episode(episode_batch)
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
# # flatten episode batch
# o = episode_batch['o']#[:, :-1, :]
# g = episode_batch['g']#[:, :-1, :]
# ag = episode_batch['ag']#[:, :-1, :]
# o = np.reshape(o, (-1, self.dimo))
# g = np.reshape(g, (-1, self.dimg))
# ag = np.reshape(ag, (-1, self.dimg))
# o, g = self._preprocess_og(o, ag, g)
#
# self.o_stats.update(o)
# self.g_stats.update(g)
#
# self.o_stats.recompute_stats()
# self.g_stats.recompute_stats()
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
transitions = self.sample_transitions(episode_batch,
num_normalizing_transitions)
o, g, ag = transitions['o'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def _preprocess_u(self, u):
return np.clip(u, -self.max_u, self.max_u)
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
'V_fun', 'V_target_fun', 'lock', 'env', 'sample_transitions',
'stage_shapes', 'create_v_function'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([not subname in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
if 'use_Q' not in state:
state['use_Q'] = False # a hack to accomendate old data
if 'create_v_function' in state:
del state['create_v_function']
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert (len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
def save(self, save_path):
tf_util.save_variables(save_path)
class PGGD(object):
DIMO = 0
@store_args
def __init__(self,
input_dims,
buffer_size,
hidden,
layers,
network_class,
polyak,
batch_size,
Q_lr,
pi_lr,
norm_eps,
norm_clip,
max_u,
action_l2,
clip_obs,
scope,
T,
rollout_batch_size,
subtract_goals,
relative_goals,
clip_pos_returns,
clip_return,
sample_transitions,
gamma,
reuse=False,
**kwargs):
"""Implementation of PGGD that is used in combination with Hindsight Experience Replay (HER).
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per PGGD agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
# ------------------
# To access information of environment name and stuff
self.kwargs = kwargs
# ------------------
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# ----------------------
input_shapes['o'] = (None, )
# ----------------------
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None, )
# ----------------------
stage_shapes['G'] = (None, )
# ----------------------
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {
key: (self.T, *input_shapes[key]) if key != 'o' else
(self.T + 1, PGGD.DIMO)
for key, val in input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T + 1, self.dimg)
# -------------------
buffer_shapes['G'] = (self.T, )
buffer_shapes['sigma'] = (self.T, self.dimu)
self.weight_path = None
# -------------------
buffer_size = (self.buffer_size //
self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T,
self.sample_transitions)
def _random_action(self, n):
return np.random.uniform(low=-self.max_u,
high=self.max_u,
size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
# -------------------------------
# If observation has more dimensions than what the policy takes in
# then just truncate it.
def get_actions(self, o, ag, g, exploit=False):
# if len(o.shape) == 1:
# o = o[:self.dimo]
# g = g[:self.dimg]
# ag = ag[:self.dimg]
# else:
# o = o[:,:self.dimo]
# g = g[:,:self.dimg]
# ag = ag[:,:self.dimg]
o, g = self._preprocess_og(o, ag, g)
policy = self.main
# values to compute
if exploit:
vals = [policy.da_tf]
else:
vals = [policy.a_tf]
vals += [policy.raw_tf, policy.sigma_tf]
# feed
feed = {
policy.o_tf:
o.reshape(-1, self.dimo),
policy.g_tf:
g.reshape(-1, self.dimg),
policy.u_tf:
np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u, raw, sigma = ret
if u.shape[0] == 1:
u = u[0]
raw = raw[0]
sigma = sigma[0]
u = u.copy()
raw = raw.copy()
sigma = sigma.copy()
return u, raw, sigma
# -------------------------------
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch)
transitions = self.sample_transitions(episode_batch,
num_normalizing_transitions)
o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions[
'g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
if 'Variation' in self.kwargs['info']['env_name']:
o = transitions['o'][:, 1:]
# o = np.concatenate([transitions['o'][:,:ENV_FEATURES],
# transitions['o'][:,ENV_FEATURES+1:]], axis=1)
else:
o = transitions['o']
self.o_stats.update(o)
self.G_stats.update(transitions['G'])
self.sigma_stats.update(transitions['sigma'])
# self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
# self.g_stats.recompute_stats()
self.G_stats.recompute_stats()
self.sigma_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
pi_loss, pi_grad, mu = self.sess.run(
[self.pi_loss_tf, self.pi_grad_tf, self.main.mu_tf])
# print(np.mean(mu), np.mean(pi_grad), np.mean(pi_loss))
return pi_loss, pi_grad
def _update(self, pi_grad):
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
transitions = self.buffer.sample(self.batch_size)
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(
o_2, ag_2, g)
transitions_batch = [
transitions[key] for key in self.stage_shapes.keys()
]
# print(transitions['G'])
return transitions_batch
def stage_batch(self, batch=None):
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
if stage:
self.stage_batch()
pi_loss, pi_grad = self._grads()
self._update(pi_grad)
# print(np.mean(pi_grad))
return pi_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a PGGD agent with action space %d x %s..." %
(self.dimu, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
o_stats_dim = self.dimo
if 'Variation' in self.kwargs['info']['env_name']:
print("Found Variation in env name")
o_stats_dim -= 1
self.o_stats = Normalizer(o_stats_dim,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# --------------
with tf.variable_scope('G_stats') as vs:
if reuse:
vs.reuse_variables()
self.G_stats = Normalizer(1,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('sigma_stats') as vs:
if reuse:
vs.reuse_variables()
self.sigma_stats = Normalizer(self.dimu,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# --------------
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([
(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())
])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# ------------
batch_tf['G'] = tf.reshape(batch_tf['G'], [
-1,
])
# ------------
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf,
net_type='main',
**self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(target_batch_tf,
net_type='target',
**self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# ---------------------------
# loss functions
log_prob = tf.reduce_sum(tf.log(
tf.clip_by_value(self.main.a_prob_tf, 1e-10, 1.0)),
axis=1)
neg_weighted_log_prob = -tf.multiply(batch_tf['G'], log_prob)
self.pi_loss_tf = tf.reduce_mean(neg_weighted_log_prob)
# https://github.com/tensorflow/tensorflow/issues/783
def replace_none_with_zero(grads, var_list):
return [
grad if grad is not None else tf.zeros_like(var)
for var, grad in zip(var_list, grads)
]
pi_grads_tf = replace_none_with_zero(
tf.gradients(self.pi_loss_tf, self._vars('main/pi')),
self._vars('main/pi'))
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf,
var_list=self._vars('main/pi'))
# ---------------------------
# optimizers
self.pi_adam = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=False)
# polyak averaging
# self.main_vars = self._vars('main/Q') + self._vars('main/pi')
# self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars(
'g_stats') + self._global_vars('G_stats') + self._global_vars(
'sigma_stats')
# self.init_target_net_op = list(
# map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars)))
# self.update_target_net_op = list(
# map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
# self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
logs += [('stats_G/mean', np.mean(self.sess.run([self.G_stats.mean])))]
logs += [('stats_G/std', np.mean(self.sess.run([self.G_stats.std])))]
logs += [('stats_stddev/mean',
np.mean(self.sess.run([self.sigma_stats.mean])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = [
'_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main',
'target', 'lock', 'env', 'sample_transitions', 'stage_shapes',
'create_actor_critic'
]
state = {
k: v
for k, v in self.__dict__.items()
if all([not subname in k for subname in excluded_subnames])
}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run(
[x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def set_sample_transitions(self, fn):
self.sample_transitions = fn
self.buffer.sample_transitions = fn
def set_obs_size(self, dims):
self.input_dims = dims
self.dimo = dims['o']
self.dimg = dims['g']
self.dimu = dims['u']
def save_weights(self, path):
self.main.save_weights(self.sess, path)
self.weight_path = path
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
self.weight_path = state['weight_path']
# Hard override...
# This is due to the fact that the directory that the weights are saved to
# might not be the same when it is loaded again
# TODO: Delete this!!!!
self.weight_path = "/Users/matt/RL/Results/5-3blocks-GPGGD-3-256/weights"
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert (len(vars) == len(state["tf"]))
node = [
tf.no_op() if 'o_stats' in var.name else tf.assign(var, val)
for var, val in zip(vars, state["tf"])
]
self.sess.run(node)
if self.weight_path != None:
print("Reading weights for sure this time!")
print(self.weight_path)
print(tf.train.latest_checkpoint(self.weight_path))
self.main.load_weights(self.sess, self.weight_path)
class DDPG(object):
@store_args
def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size,
Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T,
rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return,
sample_transitions, gamma, reuse=False, **kwargs):
"""Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER).
Args:
input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the
actions (u)
buffer_size (int): number of transitions that are stored in the replay buffer
hidden (int): number of units in the hidden layers
layers (int): number of hidden layers
network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic')
polyak (float): coefficient for Polyak-averaging of the target network
batch_size (int): batch size for training
Q_lr (float): learning rate for the Q (critic) network
pi_lr (float): learning rate for the pi (actor) network
norm_eps (float): a small value used in the normalizer to avoid numerical instabilities
norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip]
max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u]
action_l2 (float): coefficient for L2 penalty on the actions
clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs]
scope (str): the scope used for the TensorFlow graph
T (int): the time horizon for rollouts
rollout_batch_size (int): number of parallel rollouts per DDPG agent
subtract_goals (function): function that subtracts goals from each other
relative_goals (boolean): whether or not relative goals should be fed into the network
clip_pos_returns (boolean): whether or not positive returns should be clipped
clip_return (float): clip returns to be in [-clip_return, clip_return]
sample_transitions (function) function that samples from the replay buffer
gamma (float): gamma used for Q learning updates
reuse (boolean): whether or not the networks should be reused
"""
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
for key in sorted(self.input_dims.keys()):
if key.startswith('info_'):
continue
stage_shapes[key] = (None, *input_shapes[key])
for key in ['o', 'g']:
stage_shapes[key + '_2'] = stage_shapes[key]
stage_shapes['r'] = (None,)
self.stage_shapes = stage_shapes
# Create network.
with tf.variable_scope(self.scope):
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()]
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
self._create_network(reuse=reuse)
# Configure the replay buffer.
buffer_shapes = {key: (self.T if key != 'o' else self.T+1, *input_shapes[key])
for key, val in input_shapes.items()}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T+1, self.dimg)
buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
def _random_action(self, n):
return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg)
ag = ag.reshape(-1, self.dimg)
g = self.subtract_goals(g, ag)
g = g.reshape(*g_shape)
o = np.clip(o, -self.clip_obs, self.clip_obs)
g = np.clip(g, -self.clip_obs, self.clip_obs)
return o, g
def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
compute_Q=False):
o, g = self._preprocess_og(o, ag, g)
policy = self.target if use_target_net else self.main
# values to compute
vals = [policy.pi_tf]
if compute_Q:
vals += [policy.Q_pi_tf]
# feed
feed = {
policy.o_tf: o.reshape(-1, self.dimo),
policy.g_tf: g.reshape(-1, self.dimg),
policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
u = ret[0]
noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise
u += noise
u = np.clip(u, -self.max_u, self.max_u)
u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions['g'], transitions['ag']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
# No need to preprocess the o_2 and g_2 since this is only used for stats
self.o_stats.update(transitions['o'])
self.g_stats.update(transitions['g'])
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([
self.Q_loss_tf,
self.main.Q_pi_tf,
self.Q_grad_tf,
self.pi_grad_tf
])
return critic_loss, actor_loss, Q_grad, pi_grad
def _update(self, Q_grad, pi_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
transitions = self.buffer.sample(self.batch_size)
o, o_2, g = transitions['o'], transitions['o_2'], transitions['g']
ag, ag_2 = transitions['ag'], transitions['ag_2']
transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g)
transitions_batch = [transitions[key] for key in self.stage_shapes.keys()]
return transitions_batch
def stage_batch(self, batch=None):
if batch is None:
batch = self.sample_batch()
assert len(self.buffer_ph_tf) == len(batch)
self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
def train(self, stage=True):
if stage:
self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
self._update(Q_grad, pi_grad)
return critic_loss, actor_loss
def _init_target_net(self):
self.sess.run(self.init_target_net_op)
def update_target_net(self):
self.sess.run(self.update_target_net_op)
def clear_buffer(self):
self.buffer.clear_buffer()
def _vars(self, scope):
res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope)
assert len(res) > 0
return res
def _global_vars(self, scope):
res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope)
return res
def _create_network(self, reuse=False):
logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
self.sess = tf.get_default_session()
if self.sess is None:
self.sess = tf.InteractiveSession()
# running averages
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess)
with tf.variable_scope('g_stats') as vs:
if reuse:
vs.reuse_variables()
self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess)
# mini-batch sampling.
batch = self.staging_tf.get()
batch_tf = OrderedDict([(key, batch[i])
for i, key in enumerate(self.stage_shapes.keys())])
batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1])
# networks
with tf.variable_scope('main') as vs:
if reuse:
vs.reuse_variables()
self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__)
vs.reuse_variables()
with tf.variable_scope('target') as vs:
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2']
target_batch_tf['g'] = batch_tf['g_2']
self.target = self.create_actor_critic(
target_batch_tf, net_type='target', **self.__dict__)
vs.reuse_variables()
assert len(self._vars("main")) == len(self._vars("target"))
# loss functions
target_Q_pi_tf = self.target.Q_pi_tf
clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf)
target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range)
self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q'))
self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi'))
self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q'))
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi'))
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False)
self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False)
# polyak averaging
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats')
self.init_target_net_op = list(
map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list(
map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars)))
# initialize all variables
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
self._init_target_net()
def logs(self, prefix=''):
logs = []
logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))]
logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))]
logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
if prefix is not '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs
def __getstate__(self):
"""Our policies can be loaded from pkl, but after unpickling you cannot continue training.
"""
excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats',
'main', 'target', 'lock', 'env', 'sample_transitions',
'stage_shapes', 'create_actor_critic']
state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])}
state['buffer_size'] = self.buffer_size
state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name])
return state
def __setstate__(self, state):
if 'sample_transitions' not in state:
# We don't need this for playing the policy.
state['sample_transitions'] = None
self.__init__(**state)
# set up stats (they are overwritten in __init__)
for k, v in state.items():
if k[-6:] == '_stats':
self.__dict__[k] = v
# load TF variables
vars = [x for x in self._global_vars('') if 'buffer' not in x.name]
assert(len(vars) == len(state["tf"]))
node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
self.sess.run(node)
