def setup_staging_areas(self):
for idx, device in enumerate(self._devices):
with tf.device(device):
inputs = self._input.get_input_tensors()
dtypes = [x.dtype for x in inputs]
stage = StagingArea(dtypes, shapes=None)
self._stage_ops.append(stage.put(inputs))
self._areas.append(stage)
outputs = stage.get()
for vin, vout in zip(inputs, outputs):
vout.set_shape(vin.get_shape())
self._unstage_ops.append(outputs)
def stage_data(self, batch, memory_gb=1, n_threads=4):
''''''
with tf.device('/gpu:0'):
dtypes = [t.dtype for t in batch]
shapes = [t.get_shape() for t in batch]
SA = StagingArea(dtypes,
shapes=shapes,
memory_limit=memory_gb * 1e9)
get, put, clear = SA.get(), SA.put(batch), SA.clear()
tf.train.add_queue_runner(
tf.train.QueueRunner(queue=SA,
enqueue_ops=[put] * n_threads,
close_op=clear,
cancel_op=clear))
return get
def setup_staging_areas(self):
for idx, device in enumerate(self._devices):
with tf.device(device):
inputs = self._input.get_input_tensors()
dtypes = [x.dtype for x in inputs]
stage = StagingArea(dtypes, shapes=None)
self._stage_ops.append(stage.put(inputs))
self._areas.append(stage)
outputs = stage.get()
if isinstance(
outputs,
tf.Tensor): # when size=1, TF doesn't return a list
outputs = [outputs]
for vin, vout in zip(inputs, outputs):
vout.set_shape(vin.get_shape())
self._unstage_ops.append(outputs)
def _prepare_staging(self):
with tf.variable_scope('staging', reuse=tf.AUTO_REUSE):
staging_area_tf = StagingArea(
dtypes=[tf.float32 for _ in self._stage_shapes.keys()],
shapes=[(None, *shape)
for shape in self._stage_shapes.values()])
input_ph_tf = [
tf.placeholder(tf.float32, shape=(None, *shape))
for shape in self._stage_shapes.values()
]
staging_op_tf = staging_area_tf.put(input_ph_tf)
batch_tf = OrderedDict([
(key, batch_item) for key, batch_item in zip(
self._stage_shapes.keys(), staging_area_tf.get())
])
return staging_area_tf, input_ph_tf, staging_op_tf, batch_tf
class DDPG_HER_HRL_POLICY(HRL_Policy):
@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
"""
self.ep_ctr = 0
self.hist_bins = 50
self.draw_hist_freq = 3
self._reset_hists()
self.shared_pi_err_coeff = kwargs['shared_pi_err_coeff']
HRL_Policy.__init__(self, input_dims, T, rollout_batch_size, **kwargs)
self.hidden = hidden
self.layers = layers
self.max_u = max_u
self.network_class = network_class
self.sample_transitions = sample_transitions
self.scope = scope
self.subtract_goals = subtract_goals
self.relative_goals = relative_goals
self.clip_obs = clip_obs
self.Q_lr = Q_lr
self.pi_lr = pi_lr
self.batch_size = batch_size
self.buffer_size = buffer_size
self.clip_pos_returns = clip_pos_returns
self.gamma = gamma
self.polyak = polyak
self.clip_return = clip_return
self.norm_eps = norm_eps
self.norm_clip = norm_clip
self.action_l2 = action_l2
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
self.stage_shapes['gamma'] = (None, )
# 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, *self.input_shapes[key])
for key, val in self.input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T + 1, self.dimg)
buffer_shapes['p'] = (buffer_shapes['g'][0], 1)
buffer_shapes['steps'] = buffer_shapes['p']
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)
self.preproc_lr = (self.Q_lr + self.pi_lr) / 2
def _reset_hists(self):
self.hists = {"attn": None, "prob_in": None, "rnd": None}
def draw_hists(self, img_dir):
for hist_name, hist in self.hists.items():
if hist is None:
continue
step_size = 1.0 / self.hist_bins
xs = np.arange(0, 1, step_size)
hist /= (self.ep_ctr * self.T)
fig, ax = plt.subplots()
ax.bar(xs, hist, step_size)
plt.savefig(img_dir + "/{}_hist_l_{}_ep_{}.png".format(
hist_name, self.h_level, self.ep_ctr))
self._reset_hists()
if self.child_policy is not None:
self.child_policy.draw_hists(img_dir)
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,
exploit=True,
**kwargs):
# noise_eps = noise_eps if not exploit else 0.
# random_eps = random_eps if not exploit else 0.
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, 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]
q = ret[1]
noise = noise_eps * self.max_u * np.random.randn(
*u.shape) # gaussian noise
noisy_u = u + noise
u = np.clip(noisy_u, -self.max_u, self.max_u)
random_u = np.random.binomial(1, random_eps, u.shape[0]).reshape(
-1, 1) * (self._random_action(u.shape[0]) - noisy_u) # eps-greedy
u += random_u
u = u[0].copy()
self.update_hists(feed, policy)
return u, q
def update_hists(self, feed, policy):
vals = []
hist_names_to_consider = []
for hist_name, hist in self.hists.items():
if hist_name in self.main.__dict__:
hist_names_to_consider.append(hist_name)
vals.append(eval("policy.{}".format(hist_name)))
ret = self.sess.run(vals, feed_dict=feed)
for val_idx, hist_name in enumerate(hist_names_to_consider):
this_vals = ret[val_idx]
this_hists = np.histogram(this_vals, self.hist_bins, range=(0, 1))
if self.hists[hist_name] is None:
self.hists[hist_name] = this_hists[0] / this_vals.shape[1]
else:
self.hists[hist_name] += this_hists[0] / this_vals.shape[1]
def scale_and_offset_action(self, u):
scaled_u = u.copy()
scaled_u *= self.subgoal_scale
scaled_u += self.subgoal_offset
return scaled_u
def inverse_scale_and_offset_action(self, scaled_u):
u = scaled_u.copy()
u -= self.subgoal_offset
u /= self.subgoal_scale
return u
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
"""
# print("Storing Episode h-level = {}".format(self.h_level))
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 = episode_batch['u'].shape[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.o_stats.recompute_stats()
self.g_stats.update(transitions['g'])
self.g_stats.recompute_stats()
self.ep_ctr += 1
# print("Done storing Episode")
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.shared_preproc_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
critic_loss, actor_loss, preproc_loss, Q_grad, pi_grad, preproc_grad = self.sess.run(
[
self.Q_loss_tf, self.main.Q_pi_tf, self.shared_preproc_loss_tf,
self.Q_grad_tf, self.pi_grad_tf, self.shared_preproc_grad_tf
])
return critic_loss, actor_loss, preproc_loss, Q_grad, pi_grad, preproc_grad
def _update(self, Q_grad, pi_grad, preproc_grad):
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
self.shared_preproc_adam.update(preproc_grad, self.preproc_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, preproc_loss, Q_grad, pi_grad, preproc_grad = self._grads(
)
self._update(Q_grad, pi_grad, preproc_grad)
return critic_loss, actor_loss, preproc_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_HRL 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)
target_tf = tf.clip_by_value(
batch_tf['r'] +
tf.transpose(self.gamma * batch_tf['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))
self.shared_q_err_coeff = 1.0 - self.shared_pi_err_coeff
self.shared_preproc_loss_tf = (
self.shared_q_err_coeff * self.Q_loss_tf +
self.shared_pi_err_coeff * self.pi_loss_tf)
if "shared_preproc_err" in self.main.__dict__:
self.shared_preproc_loss_tf += self.main.shared_preproc_err
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'))
shared_preproc_grads_tf = tf.gradients(
self.shared_preproc_loss_tf, self._vars('main/shared_preproc'))
assert len(self._vars('main/Q')) == len(Q_grads_tf)
assert len(self._vars('main/pi')) == len(pi_grads_tf)
assert len(
self._vars('main/shared_preproc')) == len(shared_preproc_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.shared_preproc_grads_vars_tf = zip(
shared_preproc_grads_tf, self._vars('main/shared_preproc'))
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'))
self.shared_preproc_grad_tf = flatten_grads(
grads=shared_preproc_grads_tf,
var_list=self._vars('main/shared_preproc'))
# 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.shared_preproc_adam = MpiAdam(self._vars('main/shared_preproc'),
scale_grad_by_procs=False)
# polyak averaging
self.main_vars = self._vars('main/Q') + self._vars(
'main/pi') + self._vars('main/shared_preproc')
self.target_vars = self._vars('target/Q') + self._vars(
'target/pi') + self._vars('target/shared_preproc')
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='policy'):
logs = []
logs += [('buffer_size', int(self.buffer.current_size))]
logs = log_formater(logs, prefix + "_{}".format(self.h_level))
if self.child_policy is not None:
child_logs = self.child_policy.logs(prefix=prefix)
logs += child_logs
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,
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,
pre_train_model=False,
update_model=True,
feature_net_path='',
**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 auxiliary 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 auxiliary loss also called the cloning loss
"""
if self.clip_return is None:
self.clip_return = np.inf
# ADDED
self.use_contact = (self.contact_dim > 0)
self.pre_train_model = pre_train_model
self.feature_net_path = feature_net_path
self.process_type = kwargs['process_type']
self.contact_dim = kwargs['contact_dim']
self.__dict__['use_contact'] = self.use_contact
self.__dict__['pre_train'] = self.pre_train_model
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o'] - self.contact_dim
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
self.feature_dim = kwargs['feature_dim']
self.contact_point_dim = self.contact_dim // self.fixed_num_of_contact
# 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):
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dimu, self.max_u))
self.sess = tf_util.get_session()
# order: ['g', 'o', 'u', 'o_2', 'g_2', 'r'])
if self.pre_train_model == 'cpc':
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.cpc_shape = OrderedDict()
self.cpc_shape['obs_neg'] = (None, self.fixed_num_of_contact,
self.contact_point_dim)
self.cpc_shape['obs_pos'] = (None, self.fixed_num_of_contact,
self.contact_point_dim)
self.cpc_staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.cpc_shape.keys()],
shapes=list(self.cpc_shape.values()))
self.cpc_buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.cpc_shape.values()
]
self.cpc_stage_op = self.cpc_staging_tf.put(
self.cpc_buffer_ph_tf)
else:
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.update_model = update_model
if self.pre_train_model != 'none':
self.__dict__['feature_net_path'] = self.feature_net_path
self.__dict__['clip_obs'] = self.clip_obs
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)
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):
# self.clip_obs = 200
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)
if len(o.shape) == 1:
o[-self.dimo:] = np.clip(o[-self.dimo:], -self.clip_obs,
self.clip_obs)
elif len(o.shape) == 2:
o[:, -self.dimo:] = np.clip(o[:, -self.dimo:], -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]
"""lines added here, remove later"""
ori = o[:, -7:-4].copy()
noise = np.random.normal(0, 7e-4, ori.shape)
o[:, -7:-4] += noise
feed = {
policy.o_tf:
o.reshape(-1, self.dimo + self.contact_dim),
policy.g_tf:
g.reshape(-1, self.dimg),
policy.u_tf:
np.zeros((o.size // (self.dimo + self.contact_dim), self.dimu),
dtype=np.float32)
}
ret = self.sess.run(vals, feed_dict=feed)
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
"""
"""lines added, remove later"""
ori = episode_batch['o'][:, :, -7:-4].copy()
noise = np.random.normal(0, 7e-4, ori.shape)
episode_batch['o'][:, :, -7:-4] += noise
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)
# change goals here, recompute rewards
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 stat
"""Normalization stuff here. """
self.o_stats.update(transitions['o'][:, -self.o_stats.size:])
self.g_stats.update(transitions['g'])
if self.pre_train_model in ['cpc', 'curl']:
feed_dict = {self.main.o_tf: transitions['o']}
features = self.sess.run(self.main.features,
feed_dict=feed_dict)
features = np.clip(features, -self.clip_obs, self.clip_obs)
self.feature_stats.update(features)
self.feature_stats.recompute_stats()
# elif self.process_type == 'max_pool':
# feed_dict = {self.main.o_tf:transitions['o']}
# features = self.sess.run(self.main.features, feed_dict=feed_dict)
# self.feature_stats.update(features)
# self.feature_stats.recompute_stats()
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
return transitions['o']
def get_current_buffer_size(self):
return self.buffer.get_current_size()
def _sync_optimizers(self):
self.Q_adam.sync()
self.pi_adam.sync()
if self.pre_train_model == 'supervised':
self.feature_adam.sync()
elif self.pre_train_model == 'cpc':
self.cpc.sync()
elif self.pre_train_model == 'curl':
self.curl_adam.sync()
self.encoder_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) #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()
if key not in ['obs_pos', 'obs_neg']
]
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)
"""lines added, remove them later"""
ori = batch[1][:, -7:-4].copy()
noise = np.random.normal(0, 7e-4, ori.shape)
batch[1][:, -7:-4] += noise
noise = np.random.normal(0, 7e-4, ori.shape)
batch[3][:, -7:-4] += noise
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
if self.pre_train_model == 'supervised':
assert batch[1].shape[1] == 583, "must use full observations"
# 253, 251, 246, 233, 232, 220, 215, 210
# feature_loss, max_feature_loss, feature_grad = self.sess.run([self.feature_loss_tf, self.max_feature_loss, self.feature_grad_tf])
feature_loss, feature_grad = self.sess.run(
[self.feature_loss_tf, self.feature_grad_tf])
self.feature_adam.update(feature_grad, 1e-3)
self.sess.run(self.update_feature_weights_target)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
# writer = tf.summary.FileWriter("home/vioichigo/try/tactile-baselines/graph", self.sess.graph)
# print(self.sess.run(self.main.features))
# writer.close()
return feature_loss
elif self.pre_train_model == 'cpc':
# run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# run_metadata = tf.RunMetadata()
# obs = pickle.load(open('/home/vioichigo/try/tactile-baselines/dataset/HandManipulateEgg-v0/50000obs.pickle', 'rb'))
# indices = np.random.randint(obs.shape[0], size=batch[1].shape[0] * (self.main.n_neg - 1))
# obs_neg = obs[indices]
# obs_pos = batch[3][:, :self.contact_dim].reshape((-1, self.fixed_num_of_contact, self.contact_dim//self.fixed_num_of_contact))
# # self.sess.run(self.cpc_stage_op, feed_dict=dict(zip(self.cpc_buffer_ph_tf, [obs_neg, obs_pos])), options=run_options, run_metadata=run_metadata)
# first = time.time()
# # self.sess.run(self.cpc_stage_op, feed_dict=dict(zip(self.cpc_buffer_ph_tf, [obs_neg, obs_pos])))
# start = time.time()
# print("feed:", start - first)
# feed_dict = {self.cpc_inputs_tf['obs_pos']: obs_pos, self.cpc_inputs_tf['obs_neg']: obs_neg}
# # dict(zip(self.cpc_inputs_tf, [obs_neg, obs_pos]))
# cpc_loss, cpc_grad = self.sess.run([self.cpc_loss_tf, self.cpc_grad_tf], feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
# tl = timeline.Timeline(run_metadata.step_stats)
# ctf = tl.generate_chrome_trace_format()
# with open('./timeline.json', 'w') as f:
# f.write(ctf)
# now = time.time()
# print("compute_loss", now - start)
# self.cpc_adam.update(cpc_grad, 1e-3)
# print("update weights", time.time() - now)
# self.sess.run(self.update_cpc_weights_target)
# self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch)))
return 1
elif self.pre_train_model == 'curl':
curl_loss, curl_grad, encoder_grad = self.sess.run(
[self.curl_loss, self.curl_grad_tf, self.encoder_grad_tf])
self.curl_adam.update(curl_grad, 1e-3)
self.encoder_adam.update(encoder_grad, 1e-3)
self.sess.run(self.stage_op,
feed_dict=dict(zip(self.buffer_ph_tf, batch)))
self.sess.run(self.update_curl_weights_op)
return curl_loss
# return cpc_loss
def train(self, stage=True):
if stage:
if self.pre_train_model == 'none':
self.stage_batch()
else:
feature_loss = self.stage_batch()
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
self._update(Q_grad, pi_grad)
if self.pre_train_model == 'none':
return critic_loss, actor_loss
else:
return critic_loss, actor_loss, feature_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)
if self.pre_train_model == 'supervised':
if not self.update_model:
res = [x for x in res if x.name.find('predicted_pos') == -1]
elif self.pre_train_model == 'cpc':
if not self.update_model:
res = [x for x in res if x.name.find('new_cpc') == -1]
# elif self.pre_train_model == 'curl':
# res = [x for x in res if x.name.find('W') == -1]
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):
# running averages
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.increment_global_step = tf.assign_add(
self.global_step, 1, name='increment_global_step')
with tf.variable_scope('o_stats') as vs:
if reuse:
vs.reuse_variables()
"""Normalization stuff here. """
if self.use_contact and self.process_type in ['none', 'test']:
self.o_stats = Normalizer(self.dimo + self.contact_dim,
self.norm_eps,
self.norm_clip,
sess=self.sess)
else:
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)
if self.pre_train_model == 'cpc':
with tf.variable_scope('feature_stats') as vs:
if reuse:
vs.reuse_variables()
z_dim = pickle.load(
open(self.feature_net_path + 'params.pickle', 'rb'))[0]
self.feature_stats = Normalizer(z_dim,
self.norm_eps,
self.norm_clip,
sess=self.sess)
self.__dict__['feature_normalizer'] = self.feature_stats
elif self.pre_train_model == 'curl':
with tf.variable_scope('feature_stats') as vs:
if reuse:
vs.reuse_variables()
self.feature_stats = Normalizer(32,
self.norm_eps,
self.norm_clip,
sess=self.sess)
self.__dict__['feature_normalizer'] = self.feature_stats
# 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])
if self.pre_train_model == 'cpc':
cpc_batch = self.cpc_staging_tf.get()
cpc_batch_tf = OrderedDict([
(key, cpc_batch[i])
for i, key in enumerate(self.cpc_shape.keys())
])
# self.cpc_batch_tf = {}
# self.cpc_batch_tf['obs_neg'] = tf.placeholder(tf.float32, shape=(None, self.fixed_num_of_contact, self.contact_point_dim))
# self.cpc_batch_tf['obs_pos'] = tf.placeholder(tf.float32, shape=(None, self.fixed_num_of_contact, self.contact_point_dim))
# self.__dict__['cpc_inputs_tf'] = self.cpc_batch_tf
#choose only the demo buffer samples
# 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:
# reuse = False
if reuse:
vs.reuse_variables()
target_batch_tf = batch_tf.copy()
target_batch_tf['o'] = batch_tf['o_2'] #next_observations
target_batch_tf['g'] = batch_tf['g_2'] #next_goals
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))
# 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))
if self.pre_train_model == 'supervised':
self.feature_net_var = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='ddpg/main/pi/process/predicted_pos')
pos = batch_tf['o'][:, self.contact_dim:][:, -7:-4]
self.feature_loss_tf = tf.reduce_mean(
tf.square(pos - self.main.features))
# self.max_feature_loss = tf.reduce_max(tf.square(pos - self.main.features))
feature_grads_tf = tf.gradients(self.feature_loss_tf,
self.feature_net_var)
assert len(self.feature_net_var) == len(feature_grads_tf)
self.feature_grads_vars_tf = zip(feature_grads_tf,
self.feature_net_var)
self.feature_grad_tf = flatten_grads(grads=feature_grads_tf,
var_list=self.feature_net_var)
self.feature_adam = MpiAdam(self.feature_net_var,
scale_grad_by_procs=False)
target_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='ddpg/target/pi/process/predicted_pos')
self.update_feature_weights_target = [
tf.assign(new, old)
for (new, old) in zip(target_vars, self.feature_net_var)
]
elif self.pre_train_model == 'cpc':
self.cpc_var = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='ddpg/main/pi/process/new_cpc')
pos = tf.reshape(batch_tf['o_2'][:, :self.contact_dim], [
-1, self.fixed_num_of_contact,
self.contact_dim // self.fixed_num_of_contact
])
with tf.variable_scope('auxiliary'):
self.cpc_loss_tf = compute_cpc_loss(
self.main.z_dim,
self.main.pos_features,
self.main.neg_features,
self.main.next,
process_type=self.process_type,
n_neg=self.main.n_neg,
type=self.main.type)
cpc_grads_tf = tf.gradients(self.cpc_loss_tf, self.cpc_var)
assert len(self.cpc_var) == len(cpc_grads_tf)
self.cpc_grads_vars_tf = zip(cpc_grads_tf, self.cpc_var)
self.cpc_grad_tf = flatten_grads(grads=cpc_grads_tf,
var_list=self.cpc_var)
self.cpc_adam = MpiAdam(self.cpc_var, scale_grad_by_procs=False)
target_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='ddpg/target/pi/process/new_cpc')
self.update_cpc_weights_target = [
tf.assign(new, old)
for (new, old) in zip(target_vars, self.cpc_var)
]
elif self.pre_train_model == 'curl':
self.W = tf.get_variable("W",
shape=[self.main.z_dim, self.main.z_dim],
trainable=True)
self.encoder_var = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='ddpg/main/pi/curl')
self.encoder_adam = MpiAdam(self.encoder_var,
scale_grad_by_procs=False)
self.curl_var = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='ddpg/main/pi/curl') + [self.W]
# + tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='ddpg/target/pi/curl')
self.curl_adam = MpiAdam(self.curl_var, scale_grad_by_procs=False)
z_a = self.main.features
z_pos = tf.stop_gradient(self.target.features)
Wz = tf.matmul(self.W, tf.transpose(z_pos)) # (z_dim,B)
logits = tf.matmul(z_a, Wz) # (B,B)
logits = logits - tf.reduce_max(logits, 1)[:, None]
labels = tf.range(tf.shape(logits)[0])
self.curl_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=labels)
target_curl_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='ddpg/target/pi/curl')
self.update_curl_weights_op = list(
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(target_curl_vars, self.encoder_var)))
curl_grads_tf = tf.gradients(self.curl_loss, self.curl_var)
self.curl_grad_tf = flatten_grads(grads=curl_grads_tf,
var_list=self.curl_var)
encoder_grads_tf = tf.gradients(self.curl_loss, self.encoder_var)
self.encoder_grad_tf = flatten_grads(grads=encoder_grads_tf,
var_list=self.encoder_var)
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_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)
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))
class hrlTD3():
@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,
seed,
start_timesteps,
eval_freq,
max_timesteps,
expl_noise,
hrl_batch_size,
discount,
tau,
policy_noise,
noise_clip,
policy_freq,
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 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)
global demoBuffer
demoBuffer = ReplayBuffer(
buffer_shapes, buffer_size, self.T, self.sample_transitions
) #initialize the demo buffer; in the same way as the primary data buffer
################# hrl ###############
###############
# BUILD MODEL #
###############
#self.num_goal = num_goal
#self.num_action = num_action
#self.batch_size = batch_size
#state_dim = 6
#action_dim = 6
#max_action = float(env.action_space.high[0])
# Construct meta-controller and controller
'''
self.meta_controller = MetaController().type(dtype)
self.target_meta_controller = MetaController().type(dtype)
self.controller = Controller().type(dtype)
self.target_controller = Controller().type(dtype)self.dimo
'''
#self.meta_controller = TD3.TD3(state_dim, action_dim, max_action)
#self.meta_controller = TD3(self.dimo, self.dimo, max_u)
self.meta_controller = TD3(self.dimo + self.dimg, self.dimo,
self.clip_obs)
#self.controller = TD3.TD3(state_dim, action_dim, max_action)
self.controller = TD3(2 * self.dimo, self.dimu, max_u)
#self.meta_replay_memory = ReplayBuffer()
#self.ctrl_replay_memory = ReplayBuffer()
self.low_replay_buffer = H_ReplayBuffer()
self.high_replay_buffer = H_ReplayBuffer()
self.clip_obs2 = 5
def initDemoBuffer(
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, self.input_dims['info_' + key]), np.float32)
for key in info_keys
]
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):
obs.append(
[demoData['obs'][epsd][transition].get('observation')])
acts.append([demoData['acs'][epsd][transition]])
goals.append(
[demoData['obs'][epsd][transition].get('desired_goal')])
achieved_goals.append(
[demoData['obs'][epsd][transition].get('achieved_goal')])
for idx, key in enumerate(info_keys):
info_values[idx][
transition,
i] = demoData['info'][epsd][transition][key]
obs.append([demoData['obs'][epsd][self.T].get('observation')])
achieved_goals.append(
[demoData['obs'][epsd][self.T].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 demoBuffer
demoBuffer.store_episode(
episode
) # create the observation dict and append them into the demonstration buffer
print("Demo buffer size currently ", demoBuffer.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, 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()
episode.clear()
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 demoBuffer
transitionsDemo = demoBuffer.sample(
self.demo_batch_size) #sample from the demo buffer
for k, values in transitionsDemo.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 __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)
############### hrl ###################
def _random_action(self, n):
#return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
#return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n[0], self.dimu))
return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n[0]))
def _random_state(self, n):
#return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
#return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n[0], self.dimu))
return np.random.uniform(low=-self.clip_obs2,
high=self.clip_obs2,
size=(n[0]))
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_low_actions(self,
o1,
ag,
hg1,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_Q=False):
o, hg = self._preprocess_og(o1, ag, hg1)
'''
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)
'''
#jangikim
#joint_low_state_goal = np.concatenate([o, g], axis=None)
joint_low_state_goal = np.concatenate([o, hg], axis=None)
#print("joint_low_state_goal : ", joint_low_state_goal)
#ret = self.controller.select_action(self.lo_stats, np.array(joint_low_state_goal))
ret = self.controller.select_action(np.array(joint_low_state_goal))
#print(" get_low_actions ret : ", ret)
# action postprocessing
#jangikim
#u = ret[0]
u = ret
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
#u += np.random.binomial(1, random_eps, u.shape).reshape(-1, 1) * (self._random_action(u.shape) - u) # eps-greedy
u1 = np.random.binomial(1, random_eps, u.shape)
u2 = (self._random_action(u.shape) - u)
u3 = u1 * u2
u += u3
#print("get_low_actions u1 : ", u1)
#print("get_low_actions u2 : ", u2)
#print("get_low_actions u3 : ", u3)
'''
if u.shape[0] == 1:
u = u[0]
u = u.copy()
ret[0] = u
if len(ret) == 1:
return ret[0]
else:
return ret
'''
return u
def get_high_goal_gt(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) #for cliping o, g
joint_high_state = np.concatenate([o, g], axis=None)
#ret = self.meta_controller.select_action(self.uo_stats, joint_high_state)
ret = self.meta_controller.select_action(joint_high_state)
#ret = self.meta_controller.select_action(o)
u = ret
#print("get_high_goal_gt : ", u)
#print("get_high_goal_gt u : ", u)
noise = noise_eps * self.max_u * np.random.randn(
*u.shape) # gaussian noise
#noise = noise_eps * self.clip_obs2 * np.random.randn(*u.shape) # gaussian noise
#print("get_high_goal_gt noise : ", noise)
u += noise
#u = np.clip(u, -self.max_u, self.max_u)
u = np.clip(u, -self.clip_obs, self.clip_obs)
#u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy
#u += np.random.binomial(1, random_eps, u.shape).reshape(-1, 1) * (self._random_action(u.shape) - u) # eps-greedy
u1 = np.random.binomial(1, random_eps, u.shape)
u2 = (self._random_action(u.shape) - u)
#u2 = (self._random_state(u.shape) - u)
#print("get_high_goal_gt u2 : ", u2)
u3 = u1 * u2
u += u3
## print_point
#print("get_high_goal_gt final goal : ", u)
return u
def get_high_goal_gt_tilda(self, o, ag, g, o_new, o_nn_st, low_nn_at):
#print ("get_high_goal_gt_tilda low_nn_at : ", low_nn_at)
o, g = self._preprocess_og(o, ag, g) # for cliping o, g
for i in range(10):
o_nn_st[i], g = self._preprocess_og(o_nn_st[i], ag,
g) #for cliping o, g
o_new, g = self._preprocess_og(o_new, ag, g) #for cliping o_new
joint_high_state = np.concatenate([o, g], axis=None)
#ret = self.meta_controller.select_action(self.uo_stats, joint_high_state)
ret = self.meta_controller.select_action(joint_high_state)
#ret = self.meta_controller.select_action(o)
#gaussian_goals = np.empty((8, self.dims['o']), np.float32)
goals_candidate = []
# action postprocessing
#u = ret[0]
#noise = noise_eps * self.max_u * (np.random.randn(*u.shape) + (o_new - old_st)) # gaussian noise
mu = o_new - o
sigma = 0.1 * 0.5 * self.clip_obs
for x in range(8):
goals_candidate.append(
np.clip(sigma * np.random.randn(*ret.shape) + mu,
-self.clip_obs, self.clip_obs))
goals_candidate.append(mu)
goals_candidate.append(ret)
high_goal_gt_tilda_step = np.zeros((10 * 10, 10),
np.float32).reshape(10, 10, 10)
i = 0
for gi_tilda in goals_candidate:
j = 0
high_goal_gt_tilda_step[i][j] = gi_tilda
for _ in o_nn_st:
high_goal_gt_tilda_step[i][j + 1] = o_nn_st[
j] + high_goal_gt_tilda_step[i][j] - o_nn_st[j + 1]
j += 1
if j == 9:
break
i += 1
#print("goals_candidate : ", goals_candidate)
L2_norm_sum = 0
log_low_policy = []
i = 0
for gi_tilda in goals_candidate:
j = 0
#joint_low_state_goal = np.concatenate([n_o, gi_tilda], axis=None)
#joint_low_state_goal = np.concatenate([o, gi_tilda], axis=None)
for ai in low_nn_at:
joint_low_state_goal = np.concatenate(
[o_nn_st[j], high_goal_gt_tilda_step[i][j]], axis=None)
#low_ret = self.controller.select_action(self.lo_stats, np.array(joint_low_state_goal))
low_ret = self.controller.select_action(
np.array(joint_low_state_goal))
L2_norm = LA.norm(ai - low_ret)
L2_norm_sum += L2_norm * L2_norm
j += 1
i += 1
#print("-(L2_norm_sum / 2) : ", -(L2_norm_sum / 2))
log_low_policy.append(-(L2_norm_sum / 2))
L2_norm_sum = 0
max_num = np.argmax(np.asarray(log_low_policy))
#print("max_num : ", max_num)
## print_point
#print("goals_candidate[max_num] : ", goals_candidate[max_num])
return goals_candidate[max_num]
#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_Q_value(self, o, high_goal_gt, u):
o, high_goal_gt = self._preprocess_og(
o, high_goal_gt, high_goal_gt) # for cliping o, high_goal_gt
## print_point
#print("Get_Q_value u : ", u)
#print ("Get_Q_value o : ", o)
#print ("Get_Q_value high_goal_gt : ", high_goal_gt)
joint_low_state = np.concatenate([o, high_goal_gt], axis=None)
#print("Get_Q_value joint_low_state : ", joint_low_state)
#return self.controller.get_Q_value(self.lo_stats, np.array(joint_low_state), u)
return self.controller.get_Q_value(np.array(joint_low_state), u)
#def update_meta_controller(self, episode_timesteps, args, gamma=1.0):
#def update_meta_controller(self, o, ag, g, o_new, high_goal_gt_tilda, r, d, episode_timesteps):
def save_meta_controller(self, g, r, d, low_nn_st, low_nn_at,
episode_timesteps, ag):
obs, new_obs, us, rs, ds = [], [], [], [], []
self.high_replay_buffer.add(
(low_nn_st.copy(), low_nn_at.copy(), r.copy(), r.copy(), d.copy()))
def update_meta_controller(self, g, episode_timesteps, ag):
for it in range(episode_timesteps):
x, y, u, r, d = self.high_replay_buffer.sample(self.hrl_batch_size)
for i in range(self.hrl_batch_size):
obs, new_obs, us, rs, ds = [], [], [], [], []
high_goal_gt_tilda = self.get_high_goal_gt_tilda(
x[i][0], ag, g, x[i][9], x[i], y[i])
joint_high_state = np.concatenate([x[i][0], g], axis=None)
joint_high_state_new = np.concatenate([x[i][9], g], axis=None)
obs.append(joint_high_state.copy())
new_obs.append(joint_high_state_new.copy())
us.append(high_goal_gt_tilda.copy())
rs.append(r[i].copy())
ds.append(d[i].copy())
self.meta_controller.train2(np.array(obs),
np.array(new_obs),
np.array(us),
np.array(rs),
np.array(ds),
it,
self.discount,
self.tau,
self.policy_noise,
self.noise_clip,
policy_freq=2)
del obs[:]
del new_obs[:]
del us[:]
del rs[:]
del ds[:]
#def update_controller(self, episode_timesteps, args, gamma=1.0):
def update_controller(self, o, o_new, high_goal_gt, u, r, d,
episode_timesteps):
o, o_new = self._preprocess_og(o, o_new, o_new) # for cliping o, o_new
#print( " update_controller u : ", u)
#print(" update_controller u[0] : ", u[0])
joint_low_state_goal_old = np.concatenate([o, high_goal_gt], axis=None)
joint_low_state_goal_new = np.concatenate([o_new, high_goal_gt],
axis=None)
self.low_replay_buffer.add(
(joint_low_state_goal_old.copy(), joint_low_state_goal_new.copy(),
u[0], r, d))
#self.controller.train(self.lo_stats, self.low_replay_buffer, episode_timesteps, self.hrl_batch_size, self.discount, self.tau, self.policy_noise,
self.controller.train(self.low_replay_buffer,
episode_timesteps,
self.hrl_batch_size,
self.discount,
self.tau,
self.policy_noise,
self.noise_clip,
policy_freq=2)
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'])
#jangikim
po, pg = self._preprocess_og(episode_batch['o'],
episode_batch['ag'],
episode_batch['g'])
self.o_stats.update(po)
self.g_stats.update(pg)
self.o_stats.recompute_stats()
self.g_stats.recompute_stats()
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 _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)
with tf.variable_scope('uo_stats') as vs:
if reuse:
vs.reuse_variables()
self.uo_stats = Normalizer(self.dimo + self.dimg,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('lo_stats') as vs:
if reuse:
vs.reuse_variables()
self.lo_stats = Normalizer(2 * self.dimo,
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()
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 __init__(self,
use_aux_tasks,
input_dims,
image_input_shapes,
buffer_size,
hidden,
layers,
dim_latent_repr,
cnn_nonlinear,
use_bottleneck_layer,
polyak,
batch_size,
Q_lr,
pi_lr,
norm_eps,
norm_clip,
max_u,
action_l2,
scope,
T,
rollout_batch_size,
clip_pos_returns,
clip_return,
log_loss,
sample_transitions,
gamma,
rank,
serialized=False,
reuse=False,
clip_grad_range=None,
aux_filter_interval=None,
scale_grad_by_procs=False,
aux_update_interval=5,
aux_base_lr=5,
**kwargs):
""" See the documentation in main.py """
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(
'cnn_actor_critic:CNNActorCritic')
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']
if self.use_aux_tasks:
self.dim_bw_frame = self.input_dims['info_bw_frame']
self.dim_op_flow = self.input_dims['info_op_flow']
self.dim_transformed_frame = self.input_dims[
'info_transformed_frame']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
include_info = [
'info_state_obs', 'info_transformed_frame', 'info_transformation',
'info_op_flow', 'info_bw_frame'
]
for key in sorted(self.input_dims.keys()):
if key.startswith('info_') and not key in include_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):
if self.use_aux_tasks:
# Initialize OL-AUX
self.num_auxiliary_tasks = 5
self.aux_weights_lr = self.aux_base_lr * self.aux_update_interval
self.aux_weight_vector_Q_tf = tf.Variable(
initial_value=1 * tf.ones(self.num_auxiliary_tasks),
dtype=tf.float32,
name='aux_weights')
self.aux_weight_grads_buffer = []
self.log_aux_losses_Q = self.log_aux_tasks_losses_pi = None # Logging buffer for aux losses
if self.aux_filter_interval is not None:
self.all_grad_history = deque(
maxlen=self.aux_filter_interval)
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=self.reuse)
# Configure the replay buffer.
buffer_shapes = {
key:
(self.T if key != 'o' and not key.startswith('info_') 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 __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,
gg_k,
replay_strategy,
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)
self.replay_strategy = replay_strategy
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
self.max_g = kwargs['max_g']
self.d0 = kwargs['d0']
self.slope = kwargs['slope']
self.goal_lr = kwargs['goal_lr']
# reward shaping parameters
self.rshape_lambda = kwargs['rshape_lambda']
self.reshape_p = kwargs['rshape_p']
self.rshaping = kwargs['rshaping']
self.input_dims['e'] = self.dimg * self.T
self.input_dims['mask'] = self.T
self.dime = self.input_dims['e']
self.dim_mask = self.input_dims['mask']
input_shapes = dims_to_shapes(self.input_dims)
# 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)
if self.replay_strategy in [
C.REPLAY_STRATEGY_BEST_K, C.REPLAY_STRATEGY_GEN_K,
C.REPLAY_STRATEGY_GEN_K_GMM
]:
buffer_shapes['gg'] = (self.T, self.gg_k, self.dimg)
if self.replay_strategy in [
C.REPLAY_STRATEGY_BEST_K, C.REPLAY_STRATEGY_GEN_K_GMM
]:
buffer_shapes['gg_idx'] = (self.T, self.gg_k)
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)
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,
gg_k,
replay_strategy,
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)
self.replay_strategy = replay_strategy
self.dimo = self.input_dims['o']
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
self.max_g = kwargs['max_g']
self.d0 = kwargs['d0']
self.slope = kwargs['slope']
self.goal_lr = kwargs['goal_lr']
# reward shaping parameters
self.rshape_lambda = kwargs['rshape_lambda']
self.reshape_p = kwargs['rshape_p']
self.rshaping = kwargs['rshaping']
self.input_dims['e'] = self.dimg * self.T
self.input_dims['mask'] = self.T
self.dime = self.input_dims['e']
self.dim_mask = self.input_dims['mask']
input_shapes = dims_to_shapes(self.input_dims)
# 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)
if self.replay_strategy in [
C.REPLAY_STRATEGY_BEST_K, C.REPLAY_STRATEGY_GEN_K,
C.REPLAY_STRATEGY_GEN_K_GMM
]:
buffer_shapes['gg'] = (self.T, self.gg_k, self.dimg)
if self.replay_strategy in [
C.REPLAY_STRATEGY_BEST_K, C.REPLAY_STRATEGY_GEN_K_GMM
]:
buffer_shapes['gg_idx'] = (self.T, self.gg_k)
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 _preprocess_e(self, e):
e = np.clip(e, -self.clip_obs, self.clip_obs)
return e
# def td_error(self, o, g):
# vals = [self.Q_loss_tf]
def get_target_q_val(self, o, ag, g):
vals = [self.target.Q_pi_tf]
feed = {
self.target.o_tf: o.reshape(-1, self.dimo),
self.target.g_tf: g.reshape(-1, self.dimg)
}
ret = self.sess.run(vals, feed_dict=feed)
return ret[0]
def get_goals(self, u_goal, e, mask, use_target_net=False):
"""
:param u_goal: batch_size * dim_u dimensional array
:param e: batch_size * (T*dim_g) dimensional array
:param mask: batch_size * T dimensional array
:param use_target_net: True/False
:return:
"""
e = self._preprocess_e(e)
policy = self.target if use_target_net else self.main
vals = [
policy.goal_tf, policy.distance, policy.e_reshaped,
policy.goal_tf_repeated, policy.reward_sum
]
# feed
feed = {
policy.e_tf: e.reshape(-1, self.dime),
policy.mask_tf: mask.reshape(-1, self.dim_mask),
policy.u_tf: u_goal.reshape(-1, self.dimu)
}
ret = self.sess.run(vals, feed_dict=feed)
# print("Generated goal: ")
# print("Goal: ", ret[0])
# print("Distance: ", ret[1])
# print("Episode: ", ret[2])
# print("Goal repeated: ", ret[3])
# print("Reward: ", np.average(ret[4]))
# print('---------------------------------------------------------------')
# for var in self._vars('main/goal'):
# print("Name: " + var.name)
# print("Shape: " + str(var.shape))
# print(var.eval())
return ret[0]
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()
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
e = transitions['e']
transitions['e'] = self._preprocess_e(e)
self.e_stats.update(transitions['e'])
self.e_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()
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
self.goal_adam.sync()
self.Q_goal_adam.sync()
self.pi_goal_adam.sync()
def _grads(self):
# Avoid feed_dict here for performance!
tf_list = [
self.Q_loss_tf, self.main.Q_pi_tf, self.Q_grad_tf, self.pi_grad_tf
]
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
tf_list.extend([self.goal_loss_tf, self.goal_grad_tf])
tf_list.extend([self.Q_goal_loss_tf, self.Q_goal_grad_tf])
tf_list.extend([self.pi_goal_loss_tf, self.pi_goal_grad_tf])
tf_list.extend([self.main.mask_tf, self.main.d])
return self.sess.run(tf_list)
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 _update_goal(self, goal_grad, Q_goal_grad, pi_goal_grad):
# self.Q_goal_adam.update(Q_goal_grad, self.Q_lr)
# self.pi_goal_adam.update(pi_goal_grad, self.pi_lr)
self.goal_adam.update(goal_grad, self.goal_lr)
def sample_batch(self):
transitions = self.buffer.sample(self.batch_size)
return self.batch_from_transitions(transitions)
def batch_from_transitions(self, transitions):
"""
transitions is a dictionary with keys: ['o', 'ag', 'u', 'o_2', 'ag_2', 'r', 'g']
batch is a processed batch (normalizing, clipping, relative goal) for staging,
and has the keys ['o', 'ag', 'u', 'o_2', 'ag_2', 'r', g', 'g_2']
"""
# preprocess observations and goals
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)
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
e = transitions['e']
transitions['e'] = self._preprocess_e(e)
# Set the correct order of keys in the batch
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):
# print("*********************************Training*******************************")
if stage:
self.stage_batch()
if self.replay_strategy != C.REPLAY_STRATEGY_GEN_K:
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
else:
critic_loss, actor_loss, Q_grad, pi_grad,\
goal_loss, goal_grad, Q_goal_loss, Q_goal_grad, \
pi_goal_loss, pi_goal_grad, x, y = self._grads()
self._update_goal(goal_grad, Q_goal_grad, pi_goal_grad)
self._update(Q_grad, pi_grad)
# print("Loss: ", goal_loss)
# print("mask: ", np.sum(x, axis=1))
# print("distance: ", y)
# print("Reward: ", r)
# if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
# goal_loss = self.sess.run(self.target_Q_goal_tf)
# # self.goal_adam.update(goal_grad, self.goal_lr)
# print("Goal loss: ", goal_loss)
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
self.sess.run(self.copy_normal_to_goal_op)
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)
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
# running averages
with tf.variable_scope('e_stats') as vs:
if reuse:
vs.reuse_variables()
self.e_stats = Normalizer(self.dime,
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_vec = tf.square(
tf.stop_gradient(target_tf) - self.main.Q_tf)
self.Q_loss_tf = tf.reduce_mean(self.Q_loss_tf_vec)
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')
if self.replay_strategy == C.REPLAY_STRATEGY_GEN_K:
# loss functions for goal generation network
target_Q_pi_goal_tf = self.target.Q_pi_goal_tf
target_goal_tf = tf.clip_by_value(
self.main.reward + self.gamma * target_Q_pi_goal_tf,
*clip_range)
self.goal_loss_tf = -self.LAMBDA * tf.reduce_mean(
tf.square(
tf.stop_gradient(target_goal_tf) - self.main.Q_goal_tf))
# self.goal_loss_tf += 0.0 * tf.reduce_mean(tf.square(self.main.goal_tf / self.max_g))
# self.goal_loss_tf = 0
# self.reward_sum = tf.reduce_mean(self.main.reward_sum)
self.goal_loss_tf += -tf.reduce_mean(self.main.reward_sum)
# loss functions for Q_goal and pi_goal
self.Q_goal_loss_tf = tf.reduce_mean(
tf.square(
tf.stop_gradient(target_goal_tf) - self.main.Q_goal_tf))
self.pi_goal_loss_tf = -tf.reduce_mean(self.main.Q_pi_goal_tf)
self.pi_goal_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_goal_tf / self.max_u))
# gradients
goal_grads_tf = tf.gradients(self.goal_loss_tf,
self._vars('main/goal'))
self.goal_grad_tf = flatten_grads(grads=goal_grads_tf,
var_list=self._vars('main/goal'))
Q_goal_grads_tf = tf.gradients(self.Q_goal_loss_tf,
self._vars('main/gQ'))
self.Q_goal_grad_tf = flatten_grads(grads=Q_goal_grads_tf,
var_list=self._vars('main/gQ'))
pi_goal_grads_tf = tf.gradients(self.pi_goal_loss_tf,
self._vars('main/gpi'))
self.pi_goal_grad_tf = flatten_grads(
grads=pi_goal_grads_tf, var_list=self._vars('main/gpi'))
assert len(self._vars('main/goal')) == len(goal_grads_tf)
assert len(self._vars('main/gQ')) == len(Q_goal_grads_tf)
assert len(self._vars('main/gpi')) == len(pi_goal_grads_tf)
# optimizers
self.goal_adam = MpiAdam(self._vars('main/goal'),
scale_grad_by_procs=False)
self.Q_goal_adam = MpiAdam(self._vars('main/gQ'),
scale_grad_by_procs=False)
self.pi_goal_adam = MpiAdam(self._vars('main/gpi'),
scale_grad_by_procs=False)
self.main_vars += self._vars('main/goal') + self._vars(
'main/gQ') + self._vars('main/gpi')
self.target_vars += self._vars('target/goal') + self._vars(
'target/gQ') + self._vars('target/gpi')
self.stats_vars += self._global_vars('e_stats')
self.normal_vars = self._vars('main/Q') + self._vars(
'main/pi') + self._vars('target/Q') + self._vars('target/pi')
self.goal_vars = self._vars('main/gQ') + self._vars(
'main/gpi') + self._vars('target/gQ') + self._vars(
'target/gpi')
self.copy_normal_to_goal_op = list(
map(lambda v: v[0].assign(0 * v[0] + 1 * v[1]),
zip(self.goal_vars, self.normal_vars)))
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 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,
r_bias,
bias_clip_low,
bias_clip_high,
n_epochs,
ismuti,
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)
self.total_epoch_r_mean_bias = []
self.total_epoch_r_std_bias = []
self.rb = r_bias
self.bias_clip_low = bias_clip_low
self.bias_clip_high = bias_clip_high
self.isMuti = ismuti
self.epcoch_num = 0
self.isPlot = False
self.picdir = ''
self.rewdir = ''
def save_reward_pic(self, reward):
with open(self.rewdir, "wb") as fp:
pickle.dump(reward, fp)
plt.clf()
print('min:', np.min(reward))
print('max', np.max(reward))
# fig, ax = plt.subplots()
plt.figure(figsize=(10, 8))
high = min(np.max(reward), 5.)
bins = np.arange(0., high, 0.1)
plt.hist(reward,
bins,
alpha=0.5,
weights=[1. / len(reward)] * len(reward)) # alpha设置透明度，0为完全透明
font2 = {
'size': 18,
}
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel('Bias', font2)
plt.ylabel('Prob', font2)
plt.grid(True)
plt.xlim([0.0, high])
plt.savefig(self.picdir)
print('save pic path:', self.picdir)
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,
compute_r_bias=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)
if compute_r_bias:
return ret[0]
# 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 recompute_reward(self, transitions):
re_transitions = transitions['re_transitions']
T = re_transitions['u'].shape[1]
batch_size = re_transitions['u'].shape[0]
# o(256, 51, 25) u (256, 50, 4) g (256, 50, 3) future_g(256,50,3) info_is_success (256, 50, 1) o_2 (256, 50, 25) ag_2 (256, 50, 3)
re_transitions['o'] = re_transitions['o'][:, :T, :].reshape(
batch_size * T, self.dimo)
re_transitions['ag'] = re_transitions['ag'][:, :T, :].reshape(
batch_size * T, self.dimg)
re_transitions['future_g'] = re_transitions['future_g'].reshape(
batch_size * T, self.dimg)
re_transitions['g'] = re_transitions['g'].reshape(
batch_size * T, self.dimg)
re_transitions['u'] = re_transitions['u'].reshape(
batch_size * T, self.dimu)
u1 = self.get_actions(re_transitions['o'],
re_transitions['ag'],
re_transitions['future_g'],
compute_r_bias=True)
u2 = self.get_actions(re_transitions['o'],
re_transitions['ag'],
re_transitions['g'],
compute_r_bias=True)
r_b = self.rb * (np.square(LA.norm(u2 - re_transitions['u'], axis=1)) -
np.square(LA.norm(u1 - re_transitions['u'], axis=1)))
r_b = np.sum(r_b.reshape(batch_size, T), axis=1)
e_r_b = np.exp(r_b)
her_indexes = re_transitions['her_index']
other_indexes = re_transitions['other_index']
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
self.total_epoch_r_mean_bias.append(e_r_b.mean())
self.total_epoch_r_std_bias.append(e_r_b.std())
if self.isPlot:
self.save_reward_pic(e_r_b)
self.isPlot = False
if self.isMuti:
transitions['r'] *= np.clip(e_r_b, self.bias_clip_low,
self.bias_clip_high)
else: # batch projection
e_r_b = np.clip(e_r_b, self.bias_clip_low, self.bias_clip_high)
e_r_b_mean = np.mean(e_r_b[her_indexes])
transitions['r'][other_indexes] /= e_r_b_mean
del transitions['re_transitions']
del transitions['origin_g']
return transitions
def sample_batch(self):
transitions = self.buffer.sample(self.batch_size)
# lky recompute reward
transitions = self.recompute_reward(transitions)
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)
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
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)
def train_qdqn(config, log_dir, make_env, model, cleanup=False):
if cleanup:
shutil.rmtree(log_dir, ignore_errors=True)
np.random.seed(42)
tf.set_random_seed(7)
env = make_env(666)
observation_space = env.observation_space
action_space = env.action_space
env.close()
actor_queue = tf.FIFOQueue(capacity=config.queue_capacity,
dtypes=[tf.uint8, tf.int32, tf.float32, tf.uint8, tf.float32, tf.int32],
shapes=[observation_space.shape, action_space.shape, [], observation_space.shape, [], []])
batch_shape = [config.batch_size]
learner_queue = StagingArea(
dtypes=[tf.uint8, tf.int32, tf.float32, tf.uint8, tf.float32],
shapes=[
batch_shape + list(observation_space.shape),
batch_shape + list(action_space.shape),
batch_shape,
batch_shape + list(observation_space.shape),
batch_shape],
memory_limit=2**30)
coord = tf.train.Coordinator()
workers = []
learner = Learner(
learner_dir(log_dir),
observation_space,
action_space,
model,
learner_queue,
config,
create_learner_logger(log_dir))
trainer = Trainer(config, actor_queue, learner_queue, observation_space, action_space,
create_trainer_logger(log_dir))
workers.append(trainer)
for i in range(config.actor_count):
workers.append(Actor(
i,
i == 0,
make_env(i),
model,
actor_queue,
config,
create_actor_logger(log_dir, i),
create_json_logger(os.path.join(actor_dir(log_dir, i), 'episodes')),
should_render=False,))
with U.make_session(config.tf_thread_count) as session:
U.initialize(session=session)
learner.load(learner_dir(log_dir), session=session);
threads = []
for worker in workers:
worker_fn = lambda: worker.run(session, coord)
thread = threading.Thread(target=worker_fn)
thread.start()
threads.append(thread)
learner.run(session, coord)
actor_queue.close()
try:
coord.join(threads, stop_grace_period_secs=10)
except RuntimeError as e:
print("Failed to join threads: {}".format(e))
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
"""
self.ep_ctr = 0
self.hist_bins = 50
self.draw_hist_freq = 3
self._reset_hists()
self.shared_pi_err_coeff = kwargs['shared_pi_err_coeff']
HRL_Policy.__init__(self, input_dims, T, rollout_batch_size, **kwargs)
self.hidden = hidden
self.layers = layers
self.max_u = max_u
self.network_class = network_class
self.sample_transitions = sample_transitions
self.scope = scope
self.subtract_goals = subtract_goals
self.relative_goals = relative_goals
self.clip_obs = clip_obs
self.Q_lr = Q_lr
self.pi_lr = pi_lr
self.batch_size = batch_size
self.buffer_size = buffer_size
self.clip_pos_returns = clip_pos_returns
self.gamma = gamma
self.polyak = polyak
self.clip_return = clip_return
self.norm_eps = norm_eps
self.norm_clip = norm_clip
self.action_l2 = action_l2
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(self.network_class)
self.stage_shapes['gamma'] = (None, )
# 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, *self.input_shapes[key])
for key, val in self.input_shapes.items()
}
buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
buffer_shapes['ag'] = (self.T + 1, self.dimg)
buffer_shapes['p'] = (buffer_shapes['g'][0], 1)
buffer_shapes['steps'] = buffer_shapes['p']
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)
self.preproc_lr = (self.Q_lr + self.pi_lr) / 2
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,
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)
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,
seed,
start_timesteps,
eval_freq,
max_timesteps,
expl_noise,
hrl_batch_size,
discount,
tau,
policy_noise,
noise_clip,
policy_freq,
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 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)
global demoBuffer
demoBuffer = ReplayBuffer(
buffer_shapes, buffer_size, self.T, self.sample_transitions
) #initialize the demo buffer; in the same way as the primary data buffer
################# hrl ###############
###############
# BUILD MODEL #
###############
#self.num_goal = num_goal
#self.num_action = num_action
#self.batch_size = batch_size
#state_dim = 6
#action_dim = 6
#max_action = float(env.action_space.high[0])
# Construct meta-controller and controller
'''
self.meta_controller = MetaController().type(dtype)
self.target_meta_controller = MetaController().type(dtype)
self.controller = Controller().type(dtype)
self.target_controller = Controller().type(dtype)self.dimo
'''
#self.meta_controller = TD3.TD3(state_dim, action_dim, max_action)
#self.meta_controller = TD3(self.dimo, self.dimo, max_u)
self.meta_controller = TD3(self.dimo + self.dimg, self.dimo,
self.clip_obs)
#self.controller = TD3.TD3(state_dim, action_dim, max_action)
self.controller = TD3(2 * self.dimo, self.dimu, max_u)
#self.meta_replay_memory = ReplayBuffer()
#self.ctrl_replay_memory = ReplayBuffer()
self.low_replay_buffer = H_ReplayBuffer()
self.high_replay_buffer = H_ReplayBuffer()
self.clip_obs2 = 5
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)
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,
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. 'GHER.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
"""
# # print("\n\n\n\n1--", input_dims, "\n2--", buffer_size, "\n3--", hidden,
# "\n4--", layers, "\n5--", network_class, "\n6--", polyak, "\n7--", batch_size,
# "\n8--", Q_lr, "\n9--", pi_lr, "\n10--", norm_eps, "\n11--", norm_clip,
# "\n12--", max_u, "\n13--", action_l2, "\n14--", clip_obs, "\n15--", scope, "\n16--", T,
# "\n17--", rollout_batch_size, "\n18--", subtract_goals, "\n19--", relative_goals,
# "\n20--", clip_pos_returns, "\n21--", clip_return,
# "\n22--", sample_transitions, "\n23--", gamma)
"""
Example of parameter values in the FetchReach-v1 run:
Input_dims (dict of ints): {'o': 10, 'u': 4, 'g': 3, 'info_is_success': 1} (o, u, g are both input to the network)
Buffer_size (int): 1E6 (total number of experience pool samples)
Hidden (int): 256 (number of hidden layer neurons)
Layers (int): 3 (three-layer neural network)
Network_class (str): GHER.ActorCritic'
Polyak (float): 0.95 (smooth parameter updated by target-Network)
Batch_size (int): 256 (bulk size)
Q_lr (float): 0.001 (learning rate)
Pi_lr (float): 0.001 (learning rate)
Norm_eps (float): 0.01 (to avoid data overflow)
Norm_clip (float): 5 (norm_clip)
Max_u (float): 1.0 (the range of the action is [-1.0, 1.0])
Action_l2 (float): 1.0 (loss coefficient of the actor network)
Clip_obs (float): 200 (obs is limited to (-200, +200))
Scope (str): "ddpg" (scope named field used by tensorflow)
T (int): 50 (the number of cycles of interaction)
Rollout_batch_size (int): 2 (number of parallel rollouts per DDPG agent)
Subtract_goals (function): A function that preprocesses the goal, with inputs a and b, and output a-b
Relative_goals (boolean): False (true if the need for function subtract_goals processing for the goal)
Clip_pos_returns (boolean): True (Do you need to eliminate the positive return)
Clip_return (float): 50 (limit the range of return to [-clip_return, clip_return])
Sample_transitions (function): The function returned by her. The parameters are defined by config.py
Gamma (float): 0.98 (the discount factor used when Q network update)
Where sample_transition comes from the definition of HER and is a key part
"""
if self.clip_return is None:
self.clip_return = np.inf
# The creation of the network structure and calculation graph is done by the actor_critic.py file
self.create_actor_critic = import_function(self.network_class)
# Extract dimension
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o'] # 10
self.dimg = self.input_dims['g'] # 4
self.dimu = self.input_dims['u'] # 3
# print("+++", input_shapes) # {'o': (10,), 'u': (4,), 'g': (3,), 'info_is_success': (1,)}
# https://www.tensorflow.org/performance/performance_models
# StagingArea provides simpler functionality and can be executed in parallel with other phases in the CPU and GPU.
# Split the input pipeline into 3 separate parallel operations, and this is scalable to take advantage of large multi-core environments
# Define the required storage variable. Suppose self.dimo=10, self.dimg=5, self.dimu=5
# Then state_shapes={'o':(None, 10), 'g':(None, 5), 'u':(None:5)}
# Add the variable used by the target network at the same time state_shapes={'o_2':(None, 10), 'g_2': (None, 5)}
# 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, ) # Reward for scalar
self.stage_shapes = stage_shapes
# After executing self.stage_shapes =
# OrderedDict([('g', (None, 3)), ('o', (None, 10)), ('u', (None, 4)), ('o_2', (None, 10) ), ('g_2', (None, 3)), ('r', (None,))])
# Including g, o, u, target used in o_2, g_2 and reward r
# Create network.
# Create tf variables based on state_shape, including g, o, u, o_2, g_2, r
# self.buffer_ph_tf = [<tf.Tensor 'ddpg/Placeholder:0' shape=(?, 3) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_1:0' shape=(?, 10) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_2:0' shape=(?, 4) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_3:0' shape=(?, 10) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_4:0' shape=(?, 3) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_5:0' shape=(?,) dtype=float32>]
with tf.variable_scope(self.scope):
# Create a StagingArea variable
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
# Create a Tensorflow variable placeholder
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
# Correspond to the tensorflow variable and the StagingArea variable
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
#
self._create_network(reuse=reuse)
# Experience pool related operations
# When T = 50, after execution, buffer_shapes=
# {'o': (51, 10), 'u': (50, 4), 'g': (50, 3), 'info_is_success': (50, 1), 'ag': (51, 3)}
# Note that a, g, u all record all the samples experienced in a cycle, so it is 50 dimensions, but o and ag need 1 more? ? ? ?
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) #
# print("+++", buffer_shapes)
# buffer_size Is the length counted by sample
# self.buffer_size=1E6 self.rollout_batch_size=2 buffer_size=1E6
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):
"""
从 [-self.max_u, +self.max_u] Random sampling n actions
"""
return np.random.uniform(low=-self.max_u,
high=self.max_u,
size=(n, self.dimu))
def _preprocess_og(self, o, ag, g):
"""
obs, goal, achieve_goal Pretreatment
In case self.relative_goal=True， then goal = goal - achieved_goal
"""
if self.relative_goals:
g_shape = g.shape
g = g.reshape(-1, self.dimg) # Increase 1 dimension
ag = ag.reshape(-1, self.dimg) # Increase 1 dimension
g = self.subtract_goals(g, ag) # g = 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):
"""
Select the action according to the self.main network, then add Gaussian noise, clip, epsilon-greedy operation, and output the processed action
"""
# If self.relative_goal=True, then the goal is preprocessed. Otherwise only clip
o, g = self._preprocess_og(o, ag, g)
# After calling the function self._create_network of this class, the self.main network and the self.target network are created, both of which are ActorCritic objects.
policy = self.target if use_target_net else self.main # Select an action based on self.main
# actor Network output action tensor
vals = [policy.pi_tf]
# print("+++")
# print(vals.shape)
# Enter the vals of the actor output into the critic network again, and get the output as Q_pi_tf
if compute_Q:
vals += [policy.Q_pi_tf]
# The construction of feed_dict, including obs, goal and action, as input to Actor and Critic
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)
}
# Execute the current policy network, output ret. ret[0] for action, ret[1] for Q value
ret = self.sess.run(vals, feed_dict=feed)
# action postprocessing
# Add Gaussian noise to Action. np.random.randn refers to sampling from a Gaussian distribution, the noise obeys Gaussian distribution
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) # After adding noise clip
# Perform epsilon-greedy operation, epsilon for random_eps
# Np.random.binomial refers to the binomial distribution, the output is 0 or 1, and the probability of output is 1 is random_eps
# If the binomial distribution outputs 0, then u+=0 is equivalent to no operation; if the output is 1, then u = u + (random_action - u) = random_action
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, verbose=False):
"""
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
Call the store_episode function in replay_buffer to store samples for one sample period
O_stats and g_stats update and store the mean and standard deviation of obs and goal, respectively, and update them regularly
"""
# Episode_batch stores a sample of the cycle generated by generate_rollout in rollout.py
# episode_batch is a dictionary, the keys include o, g, u, ag, info, and the values of the values are respectively
# o (2, 51, 10), u (2, 50, 4), g (2, 50, 3), ag (2, 51, 3), info_is_success (2, 50, 1)
# where the first dimension is the number of workers, and the second dimension is determined by the length of the cycle.
self.buffer.store_episode(episode_batch, verbose=verbose)
# Update the mean and standard deviation of o_stats and g_stats
if update_stats:
# add transitions to normalizer
episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
episode_batch['ag_2'] = episode_batch[
'ag'][:, 1:, :] # Extract next_obs and next_state
num_normalizing_transitions = transitions_in_episode_batch(
episode_batch) # Convert period to total number of samples
# Call the sampling function in sample_transitions
# Episode_batch is a dictionary with key and element shapes respectively o (2, 51, 10) u (2, 50, 4) g (2, 50, 3) ag (2, 51, 3) info_is_success (2, 50, 1)
# o_2 (2, 50, 10) ag_2 (2, 50, 3)
# Num_normalizing_transitions=100, there are 2 workers, each worker contains 50 samples of 1 cycle
transitions = self.sample_transitions(episode_batch,
num_normalizing_transitions)
# The sampled samples are preprocessed and used to update the calculations o_stats and g_stats, defined in the Normalizer, for storing mean and std
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):
"""
Returns the number of samples in the current experience pool
"""
return self.buffer.get_current_size()
def _sync_optimizers(self):
"""
Q_adam and pi_adam are operators for updating actor networks and critic networks.
"""
self.Q_adam.sync()
self.pi_adam.sync()
def _grads(self):
"""
Return loss function and gradient
Q_loss_tf, main.Q_pi_tf, Q_grad_tf, pi_grad_tf are all defined in the _create_network function
"""
# 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):
"""
Update main Actor and Critic network
The updated op is defined in _create_network
"""
self.Q_adam.update(Q_grad, self.Q_lr)
self.pi_adam.update(pi_grad, self.pi_lr)
def sample_batch(self):
"""
Sampling is called by calling the sample function in replay_buffer.py , which is derived from the definition in her.py
The returned sample consists of batch, which is used to build the feed_dict in the self.stage_batch function.
Feed_dict will be used as input to select actions and update network parameters
Calls to sample a batch, then preprocesses o and g. The key of the sample includes o, o_2, ag, ag_2, g
"""
# Call sample and return transition to dictionary, key and val.shape
# o (256, 10) u (256, 4) g (256, 3) info_is_success (256, 1) ag (256, 3) o_2 (256, 10) ag_2 (256, 3) r (256,)
# print("In DDPG: ", self.batch_size)
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)))
# tensorboard visualization
self.tfboard_sample_batch = batch
self.tfboard_sample_tf = self.buffer_ph_tf
def train(self, stage=True):
"""
Calculate the gradient and then update
Self.stage_batch was executed before the parameter update was executed in the train to build the feed_dict used for training. This function is called.
The self.sample_batch function, which in turn calls self.buffer.sample, which calls config_her in config.py, which configures the parameters of her.py functions.
The operators in train are defined in self._create_network .
"""
if stage:
self.stage_batch(
) # Returns a feed_dict constructed using the sampling method of her.py to calculate the gradient
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, update_target_net_op is defined in the function _create_network
"""
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):
"""
Define the calculation flow graph required to calculate Actor and Critic losses
"""
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
# Define Normalizer objects for the rules obs and goal respectively
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.
# Used to store the data structure of a batch sample, which is OrderedDict. After execution, batch_tf is as follows:
# OrderedDict([('g', <tf.Tensor 'ddpg/ddpg/StagingArea_get:0' shape=(?, 3) dtype=float32>),
# ('o', <tf.Tensor 'ddpg/ddpg/StagingArea_get:1' shape=(?, 10) dtype=float32>),
# ('u', <tf.Tensor 'ddpg/ddpg/StagingArea_get:2' shape=(?, 4) dtype=float32>),
# ('o_2', <tf.Tensor 'ddpg/ddpg/StagingArea_get:3' shape=(?, 10) dtype=float32>),
# ('g_2', <tf.Tensor 'ddpg/ddpg/StagingArea_get:4' shape=(?, 3) dtype=float32>),
# ('r', <tf.Tensor 'ddpg/Reshape:0' shape=(?, 1) dtype=float32>)])
# Defined batch_tf variable will be used as input to the neural network
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])
# Create main network according to ActorCritic.py
# When creating an ActorCritic network, you don't need to explicitly pass arguments. Use self.__dict__ to assign the corresponding parameters of the DDPG class directly to the corresponding parameters of ActorCritic.
# print(self.main.__dict__)
# {'inputs_tf': OrderedDict([('g', <tf.Tensor 'ddpg/ddpg/StagingArea_get:0' shape=(?, 3) dtype=float32>), ('o', <tf.Tensor ' Ddpg/ddpg/StagingArea_get:1' shape=(?, 10) dtype=float32>), ('u', <tf.Tensor 'ddpg/ddpg/StagingArea_get:2' shape=(?, 4) dtype=float32> ), ('o_2', <tf.Tensor 'ddpg/ddpg/StagingArea_get:3' shape=(?, 10) dtype=float32>), ('g_2', <tf.Tensor 'ddpg/ddpg/StagingArea_get:4 ' shape=(?, 3) dtype=float32>), ('r', <tf.Tensor 'ddpg/Reshape:0' shape=(?, 1) dtype=float32>)]),
# 'net_type': 'main', 'reuse': False, 'buffer_size': 1000000, 'hidden': 256, 'layers': 3, 'network_class': 'GHER.actor_critic:ActorCritic',
# 'polyak': 0.95, 'batch_size': 256, 'Q_lr': 0.001, 'pi_lr': 0.001, 'norm_eps': 0.01, 'norm_clip': 5, 'max_u': 1.0,
# 'action_l2': 1.0, 'clip_obs': 200.0, 'scope': 'ddpg', 'relative_goals': False, 'input_dims': {'o': 10, 'u': 4, 'g': 3, 'info_is_success': 1},
# 'T': 50, 'clip_pos_returns': True, 'clip_return': 49.996, 'rollout_batch_size': 2, 'subtract_goals': <function simple_goal_subtract at 0x7fcf72caa510>, 'sample_transitions': <function make_sample_her_transitions.<locals>._sample_her_transitions at 0x7fcf6e2ce048>,
# 'gamma': 0.98, 'info': {'env_name': 'FetchReach-v1'}, 'use_mpi': True, 'create_actor_critic': <class 'GHER.actor_critic.ActorCritic'>,
# 'dimo': 10, 'dimg': 3, 'dimu': 4, 'stage_shapes': OrderedDict([('g', (None, 3)), ('o', (None, 10)), ('u', (None, 4)), ('o_2', (None, 10)), ('g_2', (None, 3)), ('r', (None,))]), ' Staging_tf': <tensorflow.python.ops.data_flow_ops.StagingArea object at 0x7fcf6e2dddd8>,
# 'buffer_ph_tf': [<tf.Tensor 'ddpg/Placeholder:0' shape=(?, 3) dtype=float32>, <tf.Tensor 'ddpg/Placeholder_1:0' shape=(?, 10) dtype=float32 >, <tf.Tensor 'ddpg/Placeholder_2:0' shape=(?, 4) dtype=float32>, <tf.Tensor 'ddpg/Placeholder_3:0' shape=(?, 10) dtype=float32>, <tf .Tensor 'ddpg/Placeholder_4:0' shape=(?, 3) dtype=float32>, <tf.Tensor 'ddpg/Placeholder_5:0' shape=(?,) dtype=float32>],
# 'stage_op': <tf.Operation 'ddpg/ddpg/StagingArea_put' type=Stage>, 'sess': <tensorflow.python.client.session.InteractiveSession object at 0x7fcf6e2dde10>, 'o_stats': <GHER.normalizer.Normalizer Object at 0x7fcf6e2ee940>, 'g_stats': <GHER.normalizer.Normalizer object at 0x7fcf6e2ee898>,
# 'o_tf': <tf.Tensor 'ddpg/ddpg/StagingArea_get:1' shape=(?, 10) dtype=float32>, 'g_tf': <tf.Tensor 'ddpg/ddpg/StagingArea_get:0' shape=( ?, 3) dtype=float32>, 'u_tf': <tf.Tensor 'ddpg/ddpg/StagingArea_get:2' shape=(?, 4) dtype=float32>, 'pi_tf': <tf.Tensor 'ddpg/main /pi/mul:0' shape=(?, 4) dtype=float32>, 'Q_pi_tf': <tf.Tensor 'ddpg/main/Q/_3/BiasAdd:0' shape=(?, 1) dtype=float32 >, '_input_Q': <tf.Tensor 'ddpg/main/Q/concat_1:0' shape=(?, 17) dtype=float32>, 'Q_tf': <tf.Tensor 'ddpg/main/Q/_3_1/ BiasAdd: 0' shape=(?, 1) dtype=float32>}
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()
# O_2, g_2 is used to create target network
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'] # Since the target network is used to calculate the target-Q value, o and g need to use the value of the next state.
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
# To calculate Critic's target-Q value, you need to use the Actor's target network and Critic's target network.
# target_Q_pi_tf uses the next state o_2 and g_2
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)
# The loss function of Critic is the square of the difference between target_tf and Q_tf. Note that the gradient is not passed through target_tf.
self.Q_loss_tf = tf.reduce_mean(
tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf))
# The loss function of the Actor is the opposite of the Q value obtained by the actor's output in the main network.
self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
# Add regulars to Actors
self.pi_loss_tf += self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
# Calculating the gradient
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')) # Gradient and variable name correspond
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'
) # Put together the parameters of the Actor and Critic network
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( # Target Initialization operation, the main network parameter is directly assigned to the target
map(lambda v: v[0].assign(v[1]),
zip(self.target_vars, self.main_vars)))
self.update_target_net_op = list( # In the target update operation, the main network and the target network need to be weighted according to the parameter polyak
map(
lambda v: v[0].assign(self.polyak * v[0] +
(1. - self.polyak) * v[1]),
zip(self.target_vars, self.main_vars)))
# # Tensorboard visualization
# tf.summary.scalar("Q_target-Q-mean", tf.reduce_mean(target_tf))
# tf.summary.histogram("Q_target-Q", target_tf)
# tf.summary.scalar("Q_Td-error-mean", tf.reduce_mean(target_tf - self.main.Q_tf))
# tf.summary.histogram("Q_Td-error", target_tf - self.main.Q_tf)
# tf.summary.scalar("Q_reward-mean", tf.reduce_mean(batch_tf['r']))
# tf.summary.histogram("Q_reward", batch_tf['r'])
# tf.summary.scalar("Q_loss_tf", self.Q_loss_tf)
# tf.summary.scalar("pi_loss_tf", self.pi_loss_tf)
# self.merged = tf.summary.merge_all()
# 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 tfboard_func(self, summary_writer, step):
"""
Tensorboard visualization
"""
self.sess.run(self.stage_op,
feed_dict=dict(
zip(self.tfboard_sample_tf,
self.tfboard_sample_batch)))
summary = self.sess.run(self.merged)
summary_writer.add_summary(summary, global_step=step)
print("S" + str(step), end=",")
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 updata_loss_all(self, verbose=False):
assert self.buffer.current_size > 0
idxes = np.arange(self.buffer.current_size)
print("--------------------------------------")
print("Updata All loss start...")
self.buffer.update_rnnLoss(idxes, verbose=verbose)
print("Updata All loss end ...")
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)
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. 'GHER.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
"""
# # print("\n\n\n\n1--", input_dims, "\n2--", buffer_size, "\n3--", hidden,
# "\n4--", layers, "\n5--", network_class, "\n6--", polyak, "\n7--", batch_size,
# "\n8--", Q_lr, "\n9--", pi_lr, "\n10--", norm_eps, "\n11--", norm_clip,
# "\n12--", max_u, "\n13--", action_l2, "\n14--", clip_obs, "\n15--", scope, "\n16--", T,
# "\n17--", rollout_batch_size, "\n18--", subtract_goals, "\n19--", relative_goals,
# "\n20--", clip_pos_returns, "\n21--", clip_return,
# "\n22--", sample_transitions, "\n23--", gamma)
"""
Example of parameter values in the FetchReach-v1 run:
Input_dims (dict of ints): {'o': 10, 'u': 4, 'g': 3, 'info_is_success': 1} (o, u, g are both input to the network)
Buffer_size (int): 1E6 (total number of experience pool samples)
Hidden (int): 256 (number of hidden layer neurons)
Layers (int): 3 (three-layer neural network)
Network_class (str): GHER.ActorCritic'
Polyak (float): 0.95 (smooth parameter updated by target-Network)
Batch_size (int): 256 (bulk size)
Q_lr (float): 0.001 (learning rate)
Pi_lr (float): 0.001 (learning rate)
Norm_eps (float): 0.01 (to avoid data overflow)
Norm_clip (float): 5 (norm_clip)
Max_u (float): 1.0 (the range of the action is [-1.0, 1.0])
Action_l2 (float): 1.0 (loss coefficient of the actor network)
Clip_obs (float): 200 (obs is limited to (-200, +200))
Scope (str): "ddpg" (scope named field used by tensorflow)
T (int): 50 (the number of cycles of interaction)
Rollout_batch_size (int): 2 (number of parallel rollouts per DDPG agent)
Subtract_goals (function): A function that preprocesses the goal, with inputs a and b, and output a-b
Relative_goals (boolean): False (true if the need for function subtract_goals processing for the goal)
Clip_pos_returns (boolean): True (Do you need to eliminate the positive return)
Clip_return (float): 50 (limit the range of return to [-clip_return, clip_return])
Sample_transitions (function): The function returned by her. The parameters are defined by config.py
Gamma (float): 0.98 (the discount factor used when Q network update)
Where sample_transition comes from the definition of HER and is a key part
"""
if self.clip_return is None:
self.clip_return = np.inf
# The creation of the network structure and calculation graph is done by the actor_critic.py file
self.create_actor_critic = import_function(self.network_class)
# Extract dimension
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o'] # 10
self.dimg = self.input_dims['g'] # 4
self.dimu = self.input_dims['u'] # 3
# print("+++", input_shapes) # {'o': (10,), 'u': (4,), 'g': (3,), 'info_is_success': (1,)}
# https://www.tensorflow.org/performance/performance_models
# StagingArea provides simpler functionality and can be executed in parallel with other phases in the CPU and GPU.
# Split the input pipeline into 3 separate parallel operations, and this is scalable to take advantage of large multi-core environments
# Define the required storage variable. Suppose self.dimo=10, self.dimg=5, self.dimu=5
# Then state_shapes={'o':(None, 10), 'g':(None, 5), 'u':(None:5)}
# Add the variable used by the target network at the same time state_shapes={'o_2':(None, 10), 'g_2': (None, 5)}
# 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, ) # Reward for scalar
self.stage_shapes = stage_shapes
# After executing self.stage_shapes =
# OrderedDict([('g', (None, 3)), ('o', (None, 10)), ('u', (None, 4)), ('o_2', (None, 10) ), ('g_2', (None, 3)), ('r', (None,))])
# Including g, o, u, target used in o_2, g_2 and reward r
# Create network.
# Create tf variables based on state_shape, including g, o, u, o_2, g_2, r
# self.buffer_ph_tf = [<tf.Tensor 'ddpg/Placeholder:0' shape=(?, 3) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_1:0' shape=(?, 10) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_2:0' shape=(?, 4) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_3:0' shape=(?, 10) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_4:0' shape=(?, 3) dtype=float32>,
# <tf.Tensor 'ddpg/Placeholder_5:0' shape=(?,) dtype=float32>]
with tf.variable_scope(self.scope):
# Create a StagingArea variable
self.staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.stage_shapes.keys()],
shapes=list(self.stage_shapes.values()))
# Create a Tensorflow variable placeholder
self.buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.stage_shapes.values()
]
# Correspond to the tensorflow variable and the StagingArea variable
self.stage_op = self.staging_tf.put(self.buffer_ph_tf)
#
self._create_network(reuse=reuse)
# Experience pool related operations
# When T = 50, after execution, buffer_shapes=
# {'o': (51, 10), 'u': (50, 4), 'g': (50, 3), 'info_is_success': (50, 1), 'ag': (51, 3)}
# Note that a, g, u all record all the samples experienced in a cycle, so it is 50 dimensions, but o and ag need 1 more? ? ? ?
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) #
# print("+++", buffer_shapes)
# buffer_size Is the length counted by sample
# self.buffer_size=1E6 self.rollout_batch_size=2 buffer_size=1E6
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)
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)
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,
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
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
"""
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.T = T
self.subtract_goals = subtract_goals
self.relative_goals = relative_goals
self.clip_pos_returns = clip_pos_returns
if clip_return is None:
self.clip_return = np.inf
else:
self.clip_return = clip_return
self.bc_loss = bc_loss
self.q_filter = q_filter
self.num_demo = num_demo
self.demo_batch_size = demo_batch_size
self.prm_loss_weight = prm_loss_weight
self.aux_loss_weight = aux_loss_weight
self.sample_transitions = sample_transitions
self.gamma = gamma
self.kwargs = kwargs
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['ag'] = (self.T, self.dimg)
self.buffer = ReplayBuffer(buffer_shapes, self.buffer_size, self.T,
self.sample_transitions)
global DEMO_BUFFER
# initialize the demo buffer; in the same way as the primary data buffer
DEMO_BUFFER = ReplayBuffer(buffer_shapes, self.buffer_size, self.T,
self.sample_transitions)
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
class DDPG(object):
@store_args
def __init__(self,
use_aux_tasks,
input_dims,
image_input_shapes,
buffer_size,
hidden,
layers,
dim_latent_repr,
cnn_nonlinear,
use_bottleneck_layer,
polyak,
batch_size,
Q_lr,
pi_lr,
norm_eps,
norm_clip,
max_u,
action_l2,
scope,
T,
rollout_batch_size,
clip_pos_returns,
clip_return,
log_loss,
sample_transitions,
gamma,
rank,
serialized=False,
reuse=False,
clip_grad_range=None,
aux_filter_interval=None,
scale_grad_by_procs=False,
aux_update_interval=5,
aux_base_lr=5,
**kwargs):
""" See the documentation in main.py """
if self.clip_return is None:
self.clip_return = np.inf
self.create_actor_critic = import_function(
'cnn_actor_critic:CNNActorCritic')
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']
if self.use_aux_tasks:
self.dim_bw_frame = self.input_dims['info_bw_frame']
self.dim_op_flow = self.input_dims['info_op_flow']
self.dim_transformed_frame = self.input_dims[
'info_transformed_frame']
# Prepare staging area for feeding data to the model.
stage_shapes = OrderedDict()
include_info = [
'info_state_obs', 'info_transformed_frame', 'info_transformation',
'info_op_flow', 'info_bw_frame'
]
for key in sorted(self.input_dims.keys()):
if key.startswith('info_') and not key in include_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):
if self.use_aux_tasks:
# Initialize OL-AUX
self.num_auxiliary_tasks = 5
self.aux_weights_lr = self.aux_base_lr * self.aux_update_interval
self.aux_weight_vector_Q_tf = tf.Variable(
initial_value=1 * tf.ones(self.num_auxiliary_tasks),
dtype=tf.float32,
name='aux_weights')
self.aux_weight_grads_buffer = []
self.log_aux_losses_Q = self.log_aux_tasks_losses_pi = None # Logging buffer for aux losses
if self.aux_filter_interval is not None:
self.all_grad_history = deque(
maxlen=self.aux_filter_interval)
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=self.reuse)
# Configure the replay buffer.
buffer_shapes = {
key:
(self.T if key != 'o' and not key.startswith('info_') 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 get_actions(self,
o,
ag,
g,
noise_eps=0.,
random_eps=0.,
use_target_net=False,
compute_Q=False):
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'] = o.copy(), g.copy()
# 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()
if self.use_aux_tasks:
self.bw_frame_stats.update(transitions['info_bw_frame'])
self.op_flow_stats.update(transitions['info_op_flow'])
self.transformed_frame_stats.update(
transitions['info_transformed_frame'])
self.bw_frame_stats.recompute_stats()
self.op_flow_stats.recompute_stats()
self.transformed_frame_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!
assert not self.serialized
run_vars = [
self.Q_loss_tf, self.pi_loss_tf, self.Q_grad_tf, self.pi_grad_tf
]
if self.use_aux_tasks:
run_vars.append(self.main_task_Q_cnn_grad_flatten_tf)
run_vars.extend(
self.main.loss_auxiliary_tasks_Q_tf) # Q Aux losses
run_vars.extend(self.aux_Q_cnn_grads_flatten_tf) # Q Aux grads
run_vars.extend(
self.main.loss_auxiliary_tasks_pi_tf) # pi Aux losses
assert len(
self.aux_Q_cnn_grads_flatten_tf) == self.num_auxiliary_tasks
rets = self.sess.run(run_vars)
aux_losses_pi = copy.copy(rets[-self.num_auxiliary_tasks:])
aux_grads_Q = copy.copy(
rets[-2 * self.num_auxiliary_tasks:-self.num_auxiliary_tasks])
aux_losses_Q = copy.copy(rets[-3 * self.num_auxiliary_tasks:-2 *
self.num_auxiliary_tasks])
rets = rets[:-3 * self.num_auxiliary_tasks] + [aux_losses_pi] + [
aux_losses_Q
] + [aux_grads_Q]
else:
rets = self.sess.run(run_vars)
return rets
# noinspection PyAttributeOutsideInit
def train(self, stage=True):
# import cProfile, pstats, io
# pr = cProfile.Profile()
# pr.enable()
if stage:
self.stage_batch()
if self.use_aux_tasks:
critic_loss, actor_loss, Q_grad, pi_grad, main_task_grad, \
aux_losses_pi, aux_losses_Q, aux_task_grads_Q = self._grads()
self.log_aux_losses_Q = [loss for loss in aux_losses_Q]
self.log_aux_losses_pi = [loss for loss in aux_losses_pi]
self._update(Q_grad, pi_grad)
self._update_aux_weights(main_task_grad, aux_task_grads_Q)
else:
critic_loss, actor_loss, Q_grad, pi_grad = self._grads()
self._update(Q_grad, pi_grad)
# pr.disable()
# s = io.StringIO()
# ps = pstats.Stats(pr, stream=s).sort_stats('time')
# ps.print_stats(20)
# print(s.getvalue())
return critic_loss, actor_loss
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']
transitions['o'], transitions['g'] = o.copy(), g.copy()
transitions['o_2'], transitions['g_2'] = o_2.copy(), g.copy()
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 _update_aux_weights(self, main_task_grad, aux_task_grads):
"""
Called during each iteration. But only update the auxiliary task weights according to the update interval
:param main_task_grad: Gradient of the main task (of cnn)
:param aux_task_grads: A list of the gradients from each of the auxiliary tasks (of cnn)
"""
main_task_grad, aux_task_grads = self.aux_weight_updater.get_syncd_grad(
main_task_grad, aux_task_grads)
aux_weight_grad = np.zeros([self.num_auxiliary_tasks])
aux_task_grads = np.array(aux_task_grads)
main_task_grad = np.array(main_task_grad)
if self.aux_filter_interval is not None:
self.all_grad_history.append(
(main_task_grad.copy(), aux_task_grads.copy()))
main_task_grad = np.mean(np.array(
[grad[0] for grad in self.all_grad_history]),
axis=0)
aux_task_grads = np.mean(np.array(
[grad[1] for grad in self.all_grad_history]),
axis=0)
for i, aux_task_grad in enumerate(aux_task_grads):
aux_weight_grad[i] = self.Q_lr * np.dot(aux_task_grad,
main_task_grad)
self.aux_weight_grads_buffer.append(aux_weight_grad)
if len(self.aux_weight_grads_buffer) == self.aux_update_interval:
aggregate_aux_weight_grad = np.mean(np.array(
self.aux_weight_grads_buffer),
axis=0)
self.aux_weight_updater.update(self.aux_weights_lr *
aggregate_aux_weight_grad)
self.aux_weight_grads_buffer = []
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)
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)
if self.use_aux_tasks:
with tf.variable_scope('bw_frame_stats') as vs:
if reuse:
vs.reuse_variables()
self.bw_frame_stats = Normalizer(self.dim_bw_frame,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('op_flow_stats') as vs:
if reuse:
vs.reuse_variables()
self.op_flow_stats = Normalizer(self.dim_op_flow,
self.norm_eps,
self.norm_clip,
sess=self.sess)
with tf.variable_scope('transformed_frame_stats') as vs:
if reuse:
vs.reuse_variables()
self.transformed_frame_stats = Normalizer(
self.dim_transformed_frame,
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__)
if self.use_aux_tasks:
self.main.build_auxiliary_tasks()
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()
# 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)
if self.use_aux_tasks and self.log_loss:
self.pi_loss_tf = tf.clip_by_value(
self.pi_loss_tf,
np.finfo(float).eps, np.Inf) # So that log can be applied
self.Q_loss_tf = tf.log(self.Q_loss_tf)
self.pi_loss_tf = tf.log(self.pi_loss_tf)
self.action_l2_loss_tf = self.action_l2 * tf.reduce_mean(
tf.square(self.main.pi_tf / self.max_u))
self.pi_loss_tf += self.action_l2_loss_tf
if self.use_aux_tasks:
if self.log_loss:
for i, loss_tf in enumerate(
self.main.loss_auxiliary_tasks_Q_tf):
self.Q_loss_tf += tf.stop_gradient(
self.aux_weight_vector_Q_tf[i]) * tf.log(
loss_tf + self.log_min_loss)
# Use the same weight of the auxiliary tasks in Q function also for pi.
# Also possible to use separate aux weight vectors for Q and pi
for i, loss_tf in enumerate(
self.main.loss_auxiliary_tasks_pi_tf):
self.pi_loss_tf += tf.stop_gradient(
self.aux_weight_vector_Q_tf[i]) * tf.log(
loss_tf + self.log_min_loss)
else:
for i, loss_tf in enumerate(
self.main.loss_auxiliary_tasks_Q_tf):
self.Q_loss_tf += tf.stop_gradient(
self.aux_weight_vector_Q_tf[i]) * loss_tf
for i, loss_tf in enumerate(
self.main.loss_auxiliary_tasks_pi_tf):
self.pi_loss_tf += tf.stop_gradient(
self.aux_weight_vector_Q_tf[i]) * loss_tf
Q_grads_tf = tf.gradients(self.Q_loss_tf,
self._vars('main/Q'),
name='Q_gradient')
pi_grads_tf = tf.gradients(self.pi_loss_tf,
self._vars('main/pi'),
name='pi_gradient')
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'),
clip_grad_range=self.clip_grad_range)
self.pi_grad_tf = flatten_grads(grads=pi_grads_tf,
var_list=self._vars('main/pi'),
clip_grad_range=self.clip_grad_range)
# optimizers
self.Q_adam = MpiAdam(self._vars('main/Q'),
scale_grad_by_procs=self.scale_grad_by_procs)
self.pi_adam = MpiAdam(self._vars('main/pi'),
scale_grad_by_procs=self.scale_grad_by_procs)
if self.use_aux_tasks:
self.aux_weight_updater = MpiAuxUpdate(self._vars('aux_weights'),
scale_grad_by_procs=True)
if self.use_aux_tasks:
# Get gradient from the auxiliary tasks w.r.t. the shared cnn
if self.log_loss:
aux_Q_cnn_grads_tf = [
tf.gradients(
tf.log(loss_tf + self.log_min_loss, name=loss_name),
self._vars('main/Q/cnn'))
for (loss_tf,
loss_name) in zip(self.main.loss_auxiliary_tasks_Q_tf,
self.main.name_auxiliary_tasks)
]
else:
aux_Q_cnn_grads_tf = [
tf.gradients(loss_tf, self._vars('main/Q/cnn'))
for loss_tf in self.main.loss_auxiliary_tasks_Q_tf
]
self.aux_Q_cnn_grads_flatten_tf = [
flatten_grads(grads=aux_grad_tf,
var_list=self._vars('main/Q/cnn'),
clip_grad_range=self.clip_grad_range)
for aux_grad_tf in aux_Q_cnn_grads_tf
]
# Get gradient of cnn from the main task
self.main_task_Q_cnn_grad_tf = tf.gradients(
self.Q_loss_tf,
self._vars('main/Q/cnn'),
name='aux_update_main_gradient_Q')
self.main_task_Q_cnn_grad_flatten_tf = flatten_grads(
grads=self.main_task_Q_cnn_grad_tf,
var_list=self._vars('main/Q/cnn'),
clip_grad_range=self.clip_grad_range)
# polyak averaging, excluding the auxiliary variables
self.main_vars = self._vars('main/Q') + self._vars('main/pi')
self.main_vars = [
var for var in self.main_vars
if var not in (self._vars('main/Q/aux') +
self._vars('main/pi/aux'))
]
self.target_vars = self._vars('target/Q') + self._vars('target/pi')
assert len(self.main_vars) == len(self.target_vars)
self.stats_vars = self._global_vars('o_stats') + self._global_vars('bw_frame_stats') + \
self._global_vars('op_flow_stats') + self._global_vars('g_stats') + \
self._global_vars('transformed_frame_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)))
tf.variables_initializer(self._global_vars('')).run()
self._sync_optimizers()
if self.use_aux_tasks:
self.aux_weight_updater.sync()
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])))]
transitions = self.buffer.sample(self.batch_size)
action_mean = np.mean(np.abs(transitions['u']))
action_std = np.std(transitions['u'])
logs += [('buffer_a/abs_mean', action_mean)]
logs += [('buffer_a/std', action_std)]
if self.use_aux_tasks:
# Log auxiliary task losses (After the log operator)
for (aux_task_name,
aux_task_weight) in zip(self.main.name_auxiliary_tasks,
self.log_aux_losses_Q):
logs += [('aux_losses_Q/' + aux_task_name, aux_task_weight)]
# Log auxiliary task weights
curr_aux_weights = self.sess.run(self.aux_weight_vector_Q_tf)
for (aux_task_name,
aux_task_weight) in zip(self.main.name_auxiliary_tasks,
curr_aux_weights):
logs += [('aux_weights_Q/' + aux_task_name, aux_task_weight)]
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', '_updater', '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['serialized'] = True
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 __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,
pre_train_model=False,
update_model=True,
feature_net_path='',
**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 auxiliary 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 auxiliary loss also called the cloning loss
"""
if self.clip_return is None:
self.clip_return = np.inf
# ADDED
self.use_contact = (self.contact_dim > 0)
self.pre_train_model = pre_train_model
self.feature_net_path = feature_net_path
self.process_type = kwargs['process_type']
self.contact_dim = kwargs['contact_dim']
self.__dict__['use_contact'] = self.use_contact
self.__dict__['pre_train'] = self.pre_train_model
self.create_actor_critic = import_function(self.network_class)
input_shapes = dims_to_shapes(self.input_dims)
self.dimo = self.input_dims['o'] - self.contact_dim
self.dimg = self.input_dims['g']
self.dimu = self.input_dims['u']
self.feature_dim = kwargs['feature_dim']
self.contact_point_dim = self.contact_dim // self.fixed_num_of_contact
# 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):
logger.info("Creating a DDPG agent with action space %d x %s..." %
(self.dimu, self.max_u))
self.sess = tf_util.get_session()
# order: ['g', 'o', 'u', 'o_2', 'g_2', 'r'])
if self.pre_train_model == 'cpc':
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.cpc_shape = OrderedDict()
self.cpc_shape['obs_neg'] = (None, self.fixed_num_of_contact,
self.contact_point_dim)
self.cpc_shape['obs_pos'] = (None, self.fixed_num_of_contact,
self.contact_point_dim)
self.cpc_staging_tf = StagingArea(
dtypes=[tf.float32 for _ in self.cpc_shape.keys()],
shapes=list(self.cpc_shape.values()))
self.cpc_buffer_ph_tf = [
tf.placeholder(tf.float32, shape=shape)
for shape in self.cpc_shape.values()
]
self.cpc_stage_op = self.cpc_staging_tf.put(
self.cpc_buffer_ph_tf)
else:
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.update_model = update_model
if self.pre_train_model != 'none':
self.__dict__['feature_net_path'] = self.feature_net_path
self.__dict__['clip_obs'] = self.clip_obs
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)
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)
