def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
"""
Takes a Unity environment and model-specific hyper-parameters and returns the
appropriate PPO agent model for the environment.
:param brain: Brain parameters used to generate specific network graph.
:param lr: Learning rate.
:param lr_schedule: Learning rate decay schedule.
:param h_size: Size of hidden layers
:param init_entcoef: Initial value for entropy coefficient. Set lower to learn faster,
set higher to explore more.
:return: a sub-class of PPOAgent tailored to the environment.
:param max_step: Total number of training steps.
:param normalize: Whether to normalize vector observation input.
:param use_recurrent: Whether to use an LSTM layer in the network.
:param num_layers: Number of hidden layers between encoded input and policy & value layers
:param tau: Strength of soft-Q update.
:param m_size: Size of brain memory.
"""
# Create the graph here to give more granular control of the TF graph to the Optimizer.
policy.create_tf_graph()
with policy.graph.as_default():
with tf.variable_scope(""):
super().__init__(policy, trainer_params)
hyperparameters: SACSettings = cast(
SACSettings, trainer_params.hyperparameters)
lr = hyperparameters.learning_rate
lr_schedule = hyperparameters.learning_rate_schedule
max_step = trainer_params.max_steps
self.tau = hyperparameters.tau
self.init_entcoef = hyperparameters.init_entcoef
self.policy = policy
self.act_size = policy.act_size
policy_network_settings = policy.network_settings
h_size = policy_network_settings.hidden_units
num_layers = policy_network_settings.num_layers
vis_encode_type = policy_network_settings.vis_encode_type
self.tau = hyperparameters.tau
self.burn_in_ratio = 0.0
# Non-exposed SAC parameters
self.discrete_target_entropy_scale = (
0.2 # Roughly equal to e-greedy 0.05
)
self.continuous_target_entropy_scale = 1.0
stream_names = list(self.reward_signals.keys())
# Use to reduce "survivor bonus" when using Curiosity or GAIL.
self.gammas = [
_val.gamma
for _val in trainer_params.reward_signals.values()
]
self.use_dones_in_backup = {
name: tf.Variable(1.0)
for name in stream_names
}
self.disable_use_dones = {
name: self.use_dones_in_backup[name].assign(0.0)
for name in stream_names
}
if num_layers < 1:
num_layers = 1
self.target_init_op: List[tf.Tensor] = []
self.target_update_op: List[tf.Tensor] = []
self.update_batch_policy: Optional[tf.Operation] = None
self.update_batch_value: Optional[tf.Operation] = None
self.update_batch_entropy: Optional[tf.Operation] = None
self.policy_network = SACPolicyNetwork(
policy=self.policy,
m_size=self.policy.m_size, # 3x policy.m_size
h_size=h_size,
normalize=self.policy.normalize,
use_recurrent=self.policy.use_recurrent,
num_layers=num_layers,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
self.target_network = SACTargetNetwork(
policy=self.policy,
m_size=self.policy.m_size, # 1x policy.m_size
h_size=h_size,
normalize=self.policy.normalize,
use_recurrent=self.policy.use_recurrent,
num_layers=num_layers,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
# The optimizer's m_size is 3 times the policy (Q1, Q2, and Value)
self.m_size = 3 * self.policy.m_size
self._create_inputs_and_outputs()
self.learning_rate = ModelUtils.create_schedule(
lr_schedule,
lr,
self.policy.global_step,
int(max_step),
min_value=1e-10,
)
self._create_losses(
self.policy_network.q1_heads,
self.policy_network.q2_heads,
lr,
int(max_step),
stream_names,
discrete=not self.policy.use_continuous_act,
)
self._create_sac_optimizer_ops()
self.selected_actions = (self.policy.selected_actions
) # For GAIL and other reward signals
if self.policy.normalize:
target_update_norm = self.target_network.copy_normalization(
self.policy.running_mean,
self.policy.running_variance,
self.policy.normalization_steps,
)
# Update the normalization of the optimizer when the policy does.
self.policy.update_normalization_op = tf.group([
self.policy.update_normalization_op, target_update_norm
])
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
"Losses/Q1 Loss": "q1_loss",
"Losses/Q2 Loss": "q2_loss",
"Policy/Entropy Coeff": "entropy_coef",
"Policy/Learning Rate": "learning_rate",
}
self.update_dict = {
"value_loss": self.total_value_loss,
"policy_loss": self.policy_loss,
"q1_loss": self.q1_loss,
"q2_loss": self.q2_loss,
"entropy_coef": self.ent_coef,
"update_batch": self.update_batch_policy,
"update_value": self.update_batch_value,
"update_entropy": self.update_batch_entropy,
"learning_rate": self.learning_rate,
}
class SACOptimizer(TFOptimizer):
def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
"""
Takes a Unity environment and model-specific hyper-parameters and returns the
appropriate PPO agent model for the environment.
:param brain: Brain parameters used to generate specific network graph.
:param lr: Learning rate.
:param lr_schedule: Learning rate decay schedule.
:param h_size: Size of hidden layers
:param init_entcoef: Initial value for entropy coefficient. Set lower to learn faster,
set higher to explore more.
:return: a sub-class of PPOAgent tailored to the environment.
:param max_step: Total number of training steps.
:param normalize: Whether to normalize vector observation input.
:param use_recurrent: Whether to use an LSTM layer in the network.
:param num_layers: Number of hidden layers between encoded input and policy & value layers
:param tau: Strength of soft-Q update.
:param m_size: Size of brain memory.
"""
# Create the graph here to give more granular control of the TF graph to the Optimizer.
policy.create_tf_graph()
with policy.graph.as_default():
with tf.variable_scope(""):
super().__init__(policy, trainer_params)
hyperparameters: SACSettings = cast(
SACSettings, trainer_params.hyperparameters)
lr = hyperparameters.learning_rate
lr_schedule = hyperparameters.learning_rate_schedule
max_step = trainer_params.max_steps
self.tau = hyperparameters.tau
self.init_entcoef = hyperparameters.init_entcoef
self.policy = policy
self.act_size = policy.act_size
policy_network_settings = policy.network_settings
h_size = policy_network_settings.hidden_units
num_layers = policy_network_settings.num_layers
vis_encode_type = policy_network_settings.vis_encode_type
self.tau = hyperparameters.tau
self.burn_in_ratio = 0.0
# Non-exposed SAC parameters
self.discrete_target_entropy_scale = (
0.2 # Roughly equal to e-greedy 0.05
)
self.continuous_target_entropy_scale = 1.0
stream_names = list(self.reward_signals.keys())
# Use to reduce "survivor bonus" when using Curiosity or GAIL.
self.gammas = [
_val.gamma
for _val in trainer_params.reward_signals.values()
]
self.use_dones_in_backup = {
name: tf.Variable(1.0)
for name in stream_names
}
self.disable_use_dones = {
name: self.use_dones_in_backup[name].assign(0.0)
for name in stream_names
}
if num_layers < 1:
num_layers = 1
self.target_init_op: List[tf.Tensor] = []
self.target_update_op: List[tf.Tensor] = []
self.update_batch_policy: Optional[tf.Operation] = None
self.update_batch_value: Optional[tf.Operation] = None
self.update_batch_entropy: Optional[tf.Operation] = None
self.policy_network = SACPolicyNetwork(
policy=self.policy,
m_size=self.policy.m_size, # 3x policy.m_size
h_size=h_size,
normalize=self.policy.normalize,
use_recurrent=self.policy.use_recurrent,
num_layers=num_layers,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
self.target_network = SACTargetNetwork(
policy=self.policy,
m_size=self.policy.m_size, # 1x policy.m_size
h_size=h_size,
normalize=self.policy.normalize,
use_recurrent=self.policy.use_recurrent,
num_layers=num_layers,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
# The optimizer's m_size is 3 times the policy (Q1, Q2, and Value)
self.m_size = 3 * self.policy.m_size
self._create_inputs_and_outputs()
self.learning_rate = ModelUtils.create_schedule(
lr_schedule,
lr,
self.policy.global_step,
int(max_step),
min_value=1e-10,
)
self._create_losses(
self.policy_network.q1_heads,
self.policy_network.q2_heads,
lr,
int(max_step),
stream_names,
discrete=not self.policy.use_continuous_act,
)
self._create_sac_optimizer_ops()
self.selected_actions = (self.policy.selected_actions
) # For GAIL and other reward signals
if self.policy.normalize:
target_update_norm = self.target_network.copy_normalization(
self.policy.running_mean,
self.policy.running_variance,
self.policy.normalization_steps,
)
# Update the normalization of the optimizer when the policy does.
self.policy.update_normalization_op = tf.group([
self.policy.update_normalization_op, target_update_norm
])
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
"Losses/Q1 Loss": "q1_loss",
"Losses/Q2 Loss": "q2_loss",
"Policy/Entropy Coeff": "entropy_coef",
"Policy/Learning Rate": "learning_rate",
}
self.update_dict = {
"value_loss": self.total_value_loss,
"policy_loss": self.policy_loss,
"q1_loss": self.q1_loss,
"q2_loss": self.q2_loss,
"entropy_coef": self.ent_coef,
"update_batch": self.update_batch_policy,
"update_value": self.update_batch_value,
"update_entropy": self.update_batch_entropy,
"learning_rate": self.learning_rate,
}
def _create_inputs_and_outputs(self) -> None:
"""
Assign the higher-level SACModel's inputs and outputs to those of its policy or
target network.
"""
self.vector_in = self.policy.vector_in
self.visual_in = self.policy.visual_in
self.next_vector_in = self.target_network.vector_in
self.next_visual_in = self.target_network.visual_in
self.sequence_length_ph = self.policy.sequence_length_ph
self.next_sequence_length_ph = self.target_network.sequence_length_ph
if not self.policy.use_continuous_act:
self.action_masks = self.policy_network.action_masks
else:
self.output_pre = self.policy_network.output_pre
# Don't use value estimate during inference.
self.value = tf.identity(self.policy_network.value,
name="value_estimate_unused")
self.value_heads = self.policy_network.value_heads
self.dones_holder = tf.placeholder(shape=[None],
dtype=tf.float32,
name="dones_holder")
if self.policy.use_recurrent:
self.memory_in = self.policy_network.memory_in
self.memory_out = self.policy_network.memory_out
if not self.policy.use_continuous_act:
self.prev_action = self.policy_network.prev_action
self.next_memory_in = self.target_network.memory_in
def _create_losses(
self,
q1_streams: Dict[str, tf.Tensor],
q2_streams: Dict[str, tf.Tensor],
lr: tf.Tensor,
max_step: int,
stream_names: List[str],
discrete: bool = False,
) -> None:
"""
Creates training-specific Tensorflow ops for SAC models.
:param q1_streams: Q1 streams from policy network
:param q1_streams: Q2 streams from policy network
:param lr: Learning rate
:param max_step: Total number of training steps.
:param stream_names: List of reward stream names.
:param discrete: Whether or not to use discrete action losses.
"""
if discrete:
self.target_entropy = [
self.discrete_target_entropy_scale *
np.log(i).astype(np.float32) for i in self.act_size
]
discrete_action_probs = tf.exp(self.policy.all_log_probs)
per_action_entropy = discrete_action_probs * self.policy.all_log_probs
else:
self.target_entropy = (
-1 * self.continuous_target_entropy_scale *
np.prod(self.act_size[0]).astype(np.float32))
self.rewards_holders = {}
self.min_policy_qs = {}
for name in stream_names:
if discrete:
_branched_mpq1 = ModelUtils.break_into_branches(
self.policy_network.q1_pheads[name] *
discrete_action_probs,
self.act_size,
)
branched_mpq1 = tf.stack([
tf.reduce_sum(_br, axis=1, keep_dims=True)
for _br in _branched_mpq1
])
_q1_p_mean = tf.reduce_mean(branched_mpq1, axis=0)
_branched_mpq2 = ModelUtils.break_into_branches(
self.policy_network.q2_pheads[name] *
discrete_action_probs,
self.act_size,
)
branched_mpq2 = tf.stack([
tf.reduce_sum(_br, axis=1, keep_dims=True)
for _br in _branched_mpq2
])
_q2_p_mean = tf.reduce_mean(branched_mpq2, axis=0)
self.min_policy_qs[name] = tf.minimum(_q1_p_mean, _q2_p_mean)
else:
self.min_policy_qs[name] = tf.minimum(
self.policy_network.q1_pheads[name],
self.policy_network.q2_pheads[name],
)
rewards_holder = tf.placeholder(shape=[None],
dtype=tf.float32,
name=f"{name}_rewards")
self.rewards_holders[name] = rewards_holder
q1_losses = []
q2_losses = []
# Multiple q losses per stream
expanded_dones = tf.expand_dims(self.dones_holder, axis=-1)
for i, name in enumerate(stream_names):
_expanded_rewards = tf.expand_dims(self.rewards_holders[name],
axis=-1)
q_backup = tf.stop_gradient(
_expanded_rewards +
(1.0 - self.use_dones_in_backup[name] * expanded_dones) *
self.gammas[i] * self.target_network.value_heads[name])
if discrete:
# We need to break up the Q functions by branch, and update them individually.
branched_q1_stream = ModelUtils.break_into_branches(
self.policy.selected_actions * q1_streams[name],
self.act_size)
branched_q2_stream = ModelUtils.break_into_branches(
self.policy.selected_actions * q2_streams[name],
self.act_size)
# Reduce each branch into scalar
branched_q1_stream = [
tf.reduce_sum(_branch, axis=1, keep_dims=True)
for _branch in branched_q1_stream
]
branched_q2_stream = [
tf.reduce_sum(_branch, axis=1, keep_dims=True)
for _branch in branched_q2_stream
]
q1_stream = tf.reduce_mean(branched_q1_stream, axis=0)
q2_stream = tf.reduce_mean(branched_q2_stream, axis=0)
else:
q1_stream = q1_streams[name]
q2_stream = q2_streams[name]
_q1_loss = 0.5 * tf.reduce_mean(
tf.to_float(self.policy.mask) *
tf.squared_difference(q_backup, q1_stream))
_q2_loss = 0.5 * tf.reduce_mean(
tf.to_float(self.policy.mask) *
tf.squared_difference(q_backup, q2_stream))
q1_losses.append(_q1_loss)
q2_losses.append(_q2_loss)
self.q1_loss = tf.reduce_mean(q1_losses)
self.q2_loss = tf.reduce_mean(q2_losses)
# Learn entropy coefficient
if discrete:
# Create a log_ent_coef for each branch
self.log_ent_coef = tf.get_variable(
"log_ent_coef",
dtype=tf.float32,
initializer=np.log([self.init_entcoef] *
len(self.act_size)).astype(np.float32),
trainable=True,
)
else:
self.log_ent_coef = tf.get_variable(
"log_ent_coef",
dtype=tf.float32,
initializer=np.log(self.init_entcoef).astype(np.float32),
trainable=True,
)
self.ent_coef = tf.exp(self.log_ent_coef)
if discrete:
# We also have to do a different entropy and target_entropy per branch.
branched_per_action_ent = ModelUtils.break_into_branches(
per_action_entropy, self.act_size)
branched_ent_sums = tf.stack(
[
tf.reduce_sum(_lp, axis=1, keep_dims=True) + _te for _lp,
_te in zip(branched_per_action_ent, self.target_entropy)
],
axis=1,
)
self.entropy_loss = -tf.reduce_mean(
tf.to_float(self.policy.mask) * tf.reduce_mean(
self.log_ent_coef *
tf.squeeze(tf.stop_gradient(branched_ent_sums), axis=2),
axis=1,
))
# Same with policy loss, we have to do the loss per branch and average them,
# so that larger branches don't get more weight.
# The equivalent KL divergence from Eq 10 of Haarnoja et al. is also pi*log(pi) - Q
branched_q_term = ModelUtils.break_into_branches(
discrete_action_probs * self.policy_network.q1_p,
self.act_size)
branched_policy_loss = tf.stack([
tf.reduce_sum(self.ent_coef[i] * _lp - _qt,
axis=1,
keep_dims=True)
for i, (_lp, _qt) in enumerate(
zip(branched_per_action_ent, branched_q_term))
])
self.policy_loss = tf.reduce_mean(
tf.to_float(self.policy.mask) *
tf.squeeze(branched_policy_loss))
# Do vbackup entropy bonus per branch as well.
branched_ent_bonus = tf.stack([
tf.reduce_sum(self.ent_coef[i] * _lp, axis=1, keep_dims=True)
for i, _lp in enumerate(branched_per_action_ent)
])
value_losses = []
for name in stream_names:
v_backup = tf.stop_gradient(
self.min_policy_qs[name] -
tf.reduce_mean(branched_ent_bonus, axis=0))
value_losses.append(0.5 * tf.reduce_mean(
tf.to_float(self.policy.mask) * tf.squared_difference(
self.policy_network.value_heads[name], v_backup)))
else:
self.entropy_loss = -tf.reduce_mean(
self.log_ent_coef * tf.to_float(self.policy.mask) *
tf.stop_gradient(
tf.reduce_sum(
self.policy.all_log_probs + self.target_entropy,
axis=1,
keep_dims=True,
)))
batch_policy_loss = tf.reduce_mean(
self.ent_coef * self.policy.all_log_probs -
self.policy_network.q1_p,
axis=1,
)
self.policy_loss = tf.reduce_mean(
tf.to_float(self.policy.mask) * batch_policy_loss)
value_losses = []
for name in stream_names:
v_backup = tf.stop_gradient(
self.min_policy_qs[name] - tf.reduce_sum(
self.ent_coef * self.policy.all_log_probs, axis=1))
value_losses.append(0.5 * tf.reduce_mean(
tf.to_float(self.policy.mask) * tf.squared_difference(
self.policy_network.value_heads[name], v_backup)))
self.value_loss = tf.reduce_mean(value_losses)
self.total_value_loss = self.q1_loss + self.q2_loss + self.value_loss
self.entropy = self.policy_network.entropy
def _create_sac_optimizer_ops(self) -> None:
"""
Creates the Adam optimizers and update ops for SAC, including
the policy, value, and entropy updates, as well as the target network update.
"""
policy_optimizer = self.create_optimizer_op(
learning_rate=self.learning_rate, name="sac_policy_opt")
entropy_optimizer = self.create_optimizer_op(
learning_rate=self.learning_rate, name="sac_entropy_opt")
value_optimizer = self.create_optimizer_op(
learning_rate=self.learning_rate, name="sac_value_opt")
self.target_update_op = [
tf.assign(target, (1 - self.tau) * target + self.tau * source)
for target, source in zip(self.target_network.value_vars,
self.policy_network.value_vars)
]
logger.debug("value_vars")
self.print_all_vars(self.policy_network.value_vars)
logger.debug("targvalue_vars")
self.print_all_vars(self.target_network.value_vars)
logger.debug("critic_vars")
self.print_all_vars(self.policy_network.critic_vars)
logger.debug("q_vars")
self.print_all_vars(self.policy_network.q_vars)
logger.debug("policy_vars")
policy_vars = self.policy.get_trainable_variables()
self.print_all_vars(policy_vars)
self.target_init_op = [
tf.assign(target, source) for target, source in zip(
self.target_network.value_vars, self.policy_network.value_vars)
]
self.update_batch_policy = policy_optimizer.minimize(
self.policy_loss, var_list=policy_vars)
# Make sure policy is updated first, then value, then entropy.
with tf.control_dependencies([self.update_batch_policy]):
self.update_batch_value = value_optimizer.minimize(
self.total_value_loss,
var_list=self.policy_network.critic_vars)
# Add entropy coefficient optimization operation
with tf.control_dependencies([self.update_batch_value]):
self.update_batch_entropy = entropy_optimizer.minimize(
self.entropy_loss, var_list=self.log_ent_coef)
def print_all_vars(self, variables):
for _var in variables:
logger.debug(_var)
@timed
def update(self, batch: AgentBuffer,
num_sequences: int) -> Dict[str, float]:
"""
Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param batch: Experience mini-batch.
:param update_target: Whether or not to update target value network
:param reward_signal_batches: Minibatches to use for updating the reward signals,
indexed by name. If none, don't update the reward signals.
:return: Output from update process.
"""
feed_dict = self._construct_feed_dict(self.policy, batch,
num_sequences)
stats_needed = self.stats_name_to_update_name
update_stats: Dict[str, float] = {}
update_vals = self._execute_model(feed_dict, self.update_dict)
for stat_name, update_name in stats_needed.items():
update_stats[stat_name] = update_vals[update_name]
# Update target network. By default, target update happens at every policy update.
self.sess.run(self.target_update_op)
return update_stats
def update_reward_signals(self,
reward_signal_minibatches: Mapping[str,
AgentBuffer],
num_sequences: int) -> Dict[str, float]:
"""
Only update the reward signals.
:param reward_signal_batches: Minibatches to use for updating the reward signals,
indexed by name. If none, don't update the reward signals.
"""
# Collect feed dicts for all reward signals.
feed_dict: Dict[tf.Tensor, Any] = {}
update_dict: Dict[str, tf.Tensor] = {}
update_stats: Dict[str, float] = {}
stats_needed: Dict[str, str] = {}
if reward_signal_minibatches:
self.add_reward_signal_dicts(
feed_dict,
update_dict,
stats_needed,
reward_signal_minibatches,
num_sequences,
)
update_vals = self._execute_model(feed_dict, update_dict)
for stat_name, update_name in stats_needed.items():
update_stats[stat_name] = update_vals[update_name]
return update_stats
def add_reward_signal_dicts(
self,
feed_dict: Dict[tf.Tensor, Any],
update_dict: Dict[str, tf.Tensor],
stats_needed: Dict[str, str],
reward_signal_minibatches: Mapping[str, AgentBuffer],
num_sequences: int,
) -> None:
"""
Adds the items needed for reward signal updates to the feed_dict and stats_needed dict.
:param feed_dict: Feed dict needed update
:param update_dit: Update dict that needs update
:param stats_needed: Stats needed to get from the update.
:param reward_signal_minibatches: Minibatches to use for updating the reward signals,
indexed by name.
"""
for name, r_batch in reward_signal_minibatches.items():
feed_dict.update(self.reward_signals[name].prepare_update(
self.policy, r_batch, num_sequences))
update_dict.update(self.reward_signals[name].update_dict)
stats_needed.update(
self.reward_signals[name].stats_name_to_update_name)
def _construct_feed_dict(self, policy: TFPolicy, batch: AgentBuffer,
num_sequences: int) -> Dict[tf.Tensor, Any]:
"""
Builds the feed dict for updating the SAC model.
:param model: The model to update. May be different when, e.g. using multi-GPU.
:param batch: Mini-batch to use to update.
:param num_sequences: Number of LSTM sequences in batch.
"""
# Do an optional burn-in for memories
num_burn_in = int(self.burn_in_ratio * self.policy.sequence_length)
burn_in_mask = np.ones((self.policy.sequence_length), dtype=np.float32)
burn_in_mask[range(0, num_burn_in)] = 0
burn_in_mask = np.tile(burn_in_mask, num_sequences)
feed_dict = {
policy.batch_size_ph: num_sequences,
policy.sequence_length_ph: self.policy.sequence_length,
self.next_sequence_length_ph: self.policy.sequence_length,
self.policy.mask_input: batch["masks"] * burn_in_mask,
}
for name in self.reward_signals:
feed_dict[self.rewards_holders[name]] = batch[f"{name}_rewards"]
if self.policy.use_continuous_act:
feed_dict[self.policy_network.
external_action_in] = batch["continuous_action"]
else:
feed_dict[policy.output] = batch["discrete_action"]
if self.policy.use_recurrent:
feed_dict[policy.prev_action] = batch["prev_action"]
feed_dict[policy.action_masks] = batch["action_mask"]
if self.policy.use_vec_obs:
feed_dict[policy.vector_in] = batch["vector_obs"]
feed_dict[self.next_vector_in] = batch["next_vector_in"]
if self.policy.vis_obs_size > 0:
for i, _ in enumerate(policy.visual_in):
_obs = batch["visual_obs%d" % i]
feed_dict[policy.visual_in[i]] = _obs
for i, _ in enumerate(self.next_visual_in):
_obs = batch["next_visual_obs%d" % i]
feed_dict[self.next_visual_in[i]] = _obs
if self.policy.use_recurrent:
feed_dict[policy.memory_in] = [
batch["memory"][i] for i in range(0, len(batch["memory"]),
self.policy.sequence_length)
]
feed_dict[self.policy_network.memory_in] = self._make_zero_mem(
self.m_size, batch.num_experiences)
feed_dict[self.target_network.memory_in] = self._make_zero_mem(
self.m_size // 3, batch.num_experiences)
feed_dict[self.dones_holder] = batch["done"]
return feed_dict