def testMakeTimestepMaskWithPartialEpisode(self, allow_partial):
first, mid, last = ts.StepType.FIRST, ts.StepType.MID, ts.StepType.LAST
next_step_types = tf.constant([[mid, mid, last, first,
mid, mid, last, first,
mid, mid],
[mid, mid, last, first,
mid, mid, mid, mid,
mid, last]])
zeros = tf.zeros_like(next_step_types)
next_time_step = ts.TimeStep(next_step_types, zeros, zeros, zeros)
if not allow_partial:
# Mask should be 0.0 for transition timesteps (3, 7) and for all timesteps
# belonging to the final, incomplete episode.
expected_mask = [[1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
else:
# Zeros only between episodes. Incomplete episodes are valid and not
# zeroed out.
expected_mask = [[1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
timestep_mask = ppo_utils.make_timestep_mask(
next_time_step, allow_partial_episodes=allow_partial)
timestep_mask_ = self.evaluate(timestep_mask)
self.assertAllClose(expected_mask, timestep_mask_)
def _train(self, experience, weights=None):
# unpack trajectories
(time_steps, policy_steps_,
next_time_steps) = trajectory.to_transition(experience)
batch_size = nest_utils.get_outer_shape(time_steps,
self._time_step_spec)[0]
value_state = self._collect_policy.get_initial_value_state(
batch_size=batch_size)
weights = ppo_utils.make_timestep_mask(next_time_steps)
value_preds, _ = self._collect_policy.apply_value_network(
experience.observation,
experience.step_type,
value_state=value_state)
value_preds = tf.stop_gradient(value_preds)
rewards = next_time_steps.reward
# normalize rewards
if self._reward_normalizer is not None:
rewards = self._reward_normalizer.normalize(
rewards,
center_mean=False,
clip_value=self._reward_norm_clipping)
returns, normalized_advantages = compute_return_and_advantage(
self._discount_factor, self._lambda, rewards, next_time_steps,
value_preds)
policy_loss = self._update_policy(time_steps, policy_steps_,
normalized_advantages, weights)
value_loss = self._update_values(time_steps, returns, weights)
return tf_agent.LossInfo(
loss=value_loss + policy_loss,
extra=TRPOLossInfo(value_estimation_loss=value_loss,
policy_gradient_loss=policy_loss),
)
def _train(self, experience, weights):
# Get individual tensors from transitions.
(time_steps, policy_steps_,
next_time_steps) = trajectory.to_transition(experience)
actions = policy_steps_.action
if self._debug_summaries:
actions_list = tf.nest.flatten(actions)
show_action_index = len(actions_list) != 1
for i, single_action in enumerate(actions_list):
action_name = ('actions_{}'.format(i)
if show_action_index else 'actions')
tf.compat.v2.summary.histogram(name=action_name,
data=single_action,
step=self.train_step_counter)
action_distribution_parameters = policy_steps_.info
# Reconstruct per-timestep policy distribution from stored distribution
# parameters.
old_actions_distribution = (
distribution_spec.nested_distributions_from_specs(
self._action_distribution_spec,
action_distribution_parameters))
# Compute log probability of actions taken during data collection, using the
# collect policy distribution.
act_log_probs = common.log_probability(old_actions_distribution,
actions, self._action_spec)
# Compute the value predictions for states using the current value function.
# To be used for return & advantage computation.
batch_size = nest_utils.get_outer_shape(time_steps,
self._time_step_spec)[0]
policy_state = self._collect_policy.get_initial_state(
batch_size=batch_size)
value_preds, unused_policy_state = self._collect_policy.apply_value_network(
experience.observation,
experience.step_type,
policy_state=policy_state)
value_preds = tf.stop_gradient(value_preds)
valid_mask = ppo_utils.make_timestep_mask(next_time_steps)
if weights is None:
weights = valid_mask
else:
weights *= valid_mask
returns, normalized_advantages = self.compute_return_and_advantage(
next_time_steps, value_preds)
# Loss tensors across batches will be aggregated for summaries.
policy_gradient_losses = []
value_estimation_losses = []
l2_regularization_losses = []
entropy_regularization_losses = []
kl_penalty_losses = []
loss_info = None # TODO(b/123627451): Remove.
# For each epoch, create its own train op that depends on the previous one.
for i_epoch in range(self._num_epochs):
with tf.name_scope('epoch_%d' % i_epoch):
# Only save debug summaries for first and last epochs.
debug_summaries = (self._debug_summaries
and (i_epoch == 0
or i_epoch == self._num_epochs - 1))
# Build one epoch train op.
with tf.GradientTape() as tape:
loss_info = self.get_epoch_loss(
time_steps, actions, act_log_probs, returns,
normalized_advantages, action_distribution_parameters,
weights, self.train_step_counter, debug_summaries)
variables_to_train = (self._actor_net.trainable_weights +
self._value_net.trainable_weights)
grads = tape.gradient(loss_info.loss, variables_to_train)
# Tuple is used for py3, where zip is a generator producing values once.
grads_and_vars = tuple(zip(grads, variables_to_train))
if self._gradient_clipping > 0:
grads_and_vars = eager_utils.clip_gradient_norms(
grads_and_vars, self._gradient_clipping)
# If summarize_gradients, create functions for summarizing both
# gradients and variables.
if self._summarize_grads_and_vars and debug_summaries:
eager_utils.add_gradients_summaries(
grads_and_vars, self.train_step_counter)
eager_utils.add_variables_summaries(
grads_and_vars, self.train_step_counter)
self._optimizer.apply_gradients(
grads_and_vars, global_step=self.train_step_counter)
policy_gradient_losses.append(
loss_info.extra.policy_gradient_loss)
value_estimation_losses.append(
loss_info.extra.value_estimation_loss)
l2_regularization_losses.append(
loss_info.extra.l2_regularization_loss)
entropy_regularization_losses.append(
loss_info.extra.entropy_regularization_loss)
kl_penalty_losses.append(loss_info.extra.kl_penalty_loss)
# After update epochs, update adaptive kl beta, then update observation
# normalizer and reward normalizer.
batch_size = nest_utils.get_outer_shape(time_steps,
self._time_step_spec)[0]
policy_state = self._collect_policy.get_initial_state(batch_size)
# Compute the mean kl from previous action distribution.
kl_divergence = self._kl_divergence(
time_steps, action_distribution_parameters,
self._collect_policy.distribution(time_steps, policy_state).action)
self.update_adaptive_kl_beta(kl_divergence)
if self._observation_normalizer:
self._observation_normalizer.update(time_steps.observation,
outer_dims=[0, 1])
else:
# TODO(b/127661780): Verify performance of reward_normalizer when obs are
# not normalized
if self._reward_normalizer:
self._reward_normalizer.update(next_time_steps.reward,
outer_dims=[0, 1])
loss_info = tf.nest.map_structure(tf.identity, loss_info)
# Make summaries for total loss across all epochs.
# The *_losses lists will have been populated by
# calls to self.get_epoch_loss.
with tf.name_scope('Losses/'):
total_policy_gradient_loss = tf.add_n(policy_gradient_losses)
total_value_estimation_loss = tf.add_n(value_estimation_losses)
total_l2_regularization_loss = tf.add_n(l2_regularization_losses)
total_entropy_regularization_loss = tf.add_n(
entropy_regularization_losses)
total_kl_penalty_loss = tf.add_n(kl_penalty_losses)
tf.compat.v2.summary.scalar(name='policy_gradient_loss',
data=total_policy_gradient_loss,
step=self.train_step_counter)
tf.compat.v2.summary.scalar(name='value_estimation_loss',
data=total_value_estimation_loss,
step=self.train_step_counter)
tf.compat.v2.summary.scalar(name='l2_regularization_loss',
data=total_l2_regularization_loss,
step=self.train_step_counter)
tf.compat.v2.summary.scalar(name='entropy_regularization_loss',
data=total_entropy_regularization_loss,
step=self.train_step_counter)
tf.compat.v2.summary.scalar(name='kl_penalty_loss',
data=total_kl_penalty_loss,
step=self.train_step_counter)
total_abs_loss = (tf.abs(total_policy_gradient_loss) +
tf.abs(total_value_estimation_loss) +
tf.abs(total_entropy_regularization_loss) +
tf.abs(total_l2_regularization_loss) +
tf.abs(total_kl_penalty_loss))
tf.compat.v2.summary.scalar(name='total_abs_loss',
data=total_abs_loss,
step=self.train_step_counter)
if self._summarize_grads_and_vars:
with tf.name_scope('Variables/'):
all_vars = (self._actor_net.trainable_weights +
self._value_net.trainable_weights)
for var in all_vars:
tf.compat.v2.summary.histogram(
name=var.name.replace(':', '_'),
data=var,
step=self.train_step_counter)
return loss_info
def _train(self, experience, weights, train_step_counter):
# Change trajectory to transitions.
trajectory0 = nest.map_structure(lambda t: t[:, :-1], experience)
trajectory1 = nest.map_structure(lambda t: t[:, 1:], experience)
# Get individual tensors from transitions.
(time_steps, policy_steps_,
next_time_steps) = trajectory.to_transition(trajectory0, trajectory1)
actions = policy_steps_.action
if self._debug_summaries:
tf.contrib.summary.histogram('actions', actions)
action_distribution_parameters = policy_steps_.info
# Reconstruct per-timestep policy distribution from stored distribution
# parameters.
old_actions_distribution = (
distribution_spec.nested_distributions_from_specs(
self._action_distribution_spec,
action_distribution_parameters))
# Compute log probability of actions taken during data collection, using the
# collect policy distribution.
act_log_probs = common_utils.log_probability(old_actions_distribution,
actions,
self._action_spec)
# Compute the value predictions for states using the current value function.
# To be used for return & advantage computation.
batch_size = nest_utils.get_outer_shape(time_steps,
self._time_step_spec)[0]
policy_state = self._collect_policy.get_initial_state(
batch_size=batch_size)
value_preds, unused_policy_state = self._collect_policy.apply_value_network(
experience.observation,
experience.step_type,
policy_state=policy_state)
value_preds = tf.stop_gradient(value_preds)
valid_mask = ppo_utils.make_timestep_mask(next_time_steps)
if weights is None:
weights = valid_mask
else:
weights *= valid_mask
returns, normalized_advantages = self.compute_return_and_advantage(
next_time_steps, value_preds)
# Loss tensors across batches will be aggregated for summaries.
policy_gradient_losses = []
value_estimation_losses = []
l2_regularization_losses = []
entropy_regularization_losses = []
kl_penalty_losses = []
# For each epoch, create its own train op that depends on the previous one.
loss_info = tf.no_op()
for i_epoch in range(self._num_epochs):
with tf.name_scope('epoch_%d' % i_epoch):
with tf.control_dependencies(nest.flatten(loss_info)):
# Only save debug summaries for first and last epochs.
debug_summaries = (self._debug_summaries and
(i_epoch == 0
or i_epoch == self._num_epochs - 1))
# Build one epoch train op.
loss_info = self.build_train_op(
time_steps, actions, act_log_probs, returns,
normalized_advantages, action_distribution_parameters,
weights, train_step_counter,
self._summarize_grads_and_vars,
self._gradient_clipping, debug_summaries)
policy_gradient_losses.append(
loss_info.extra.policy_gradient_loss)
value_estimation_losses.append(
loss_info.extra.value_estimation_loss)
l2_regularization_losses.append(
loss_info.extra.l2_regularization_loss)
entropy_regularization_losses.append(
loss_info.extra.entropy_regularization_loss)
kl_penalty_losses.append(loss_info.extra.kl_penalty_loss)
# After update epochs, update adaptive kl beta, then update observation
# normalizer and reward normalizer.
with tf.control_dependencies(nest.flatten(loss_info)):
# Compute the mean kl from old.
batch_size = nest_utils.get_outer_shape(time_steps,
self._time_step_spec)[0]
policy_state = self._collect_policy.get_initial_state(batch_size)
kl_divergence = self._kl_divergence(
time_steps, action_distribution_parameters,
self._collect_policy.distribution(time_steps,
policy_state).action)
update_adaptive_kl_beta_op = self.update_adaptive_kl_beta(
kl_divergence)
with tf.control_dependencies([update_adaptive_kl_beta_op]):
if self._observation_normalizer:
update_obs_norm = (self._observation_normalizer.update(
time_steps.observation, outer_dims=[0, 1]))
else:
update_obs_norm = tf.no_op()
if self._reward_normalizer:
update_reward_norm = self._reward_normalizer.update(
next_time_steps.reward, outer_dims=[0, 1])
else:
update_reward_norm = tf.no_op()
with tf.control_dependencies([update_obs_norm, update_reward_norm]):
loss_info = nest.map_structure(tf.identity, loss_info)
# Make summaries for total loss across all epochs.
# The *_losses lists will have been populated by
# calls to self.build_train_op.
with tf.name_scope('Losses/'):
total_policy_gradient_loss = tf.add_n(policy_gradient_losses)
total_value_estimation_loss = tf.add_n(value_estimation_losses)
total_l2_regularization_loss = tf.add_n(l2_regularization_losses)
total_entropy_regularization_loss = tf.add_n(
entropy_regularization_losses)
total_kl_penalty_loss = tf.add_n(kl_penalty_losses)
tf.contrib.summary.scalar('policy_gradient_loss',
total_policy_gradient_loss)
tf.contrib.summary.scalar('value_estimation_loss',
total_value_estimation_loss)
tf.contrib.summary.scalar('l2_regularization_loss',
total_l2_regularization_loss)
if self._entropy_regularization:
tf.contrib.summary.scalar('entropy_regularization_loss',
total_entropy_regularization_loss)
tf.contrib.summary.scalar('kl_penalty_loss', total_kl_penalty_loss)
total_abs_loss = (tf.abs(total_policy_gradient_loss) +
tf.abs(total_value_estimation_loss) +
tf.abs(total_entropy_regularization_loss) +
tf.abs(total_l2_regularization_loss) +
tf.abs(total_kl_penalty_loss))
tf.contrib.summary.scalar('total_abs_loss', total_abs_loss)
if self._summarize_grads_and_vars:
with tf.name_scope('Variables/'):
all_vars = (self._actor_net.trainable_weights +
self._value_net.trainable_weights)
for var in all_vars:
tf.contrib.summary.histogram(var.name.replace(':', '_'),
var)
return loss_info
