def distribution(
self, time_step: ts.TimeStep, policy_state: types.NestedTensor = ()
) -> policy_step.PolicyStep:
"""Generates the distribution over next actions given the time_step.
Args:
time_step: A `TimeStep` tuple corresponding to `time_step_spec()`.
policy_state: A Tensor, or a nested dict, list or tuple of Tensors
representing the previous policy_state.
Returns:
A `PolicyStep` named tuple containing:
`action`: A tf.distribution capturing the distribution of next actions.
`state`: A policy state tensor for the next call to distribution.
`info`: Optional side information such as action log probabilities.
Raises:
ValueError or TypeError: If `validate_args is True` and inputs or
outputs do not match `time_step_spec`, `policy_state_spec`,
or `policy_step_spec`.
"""
if self._validate_args:
time_step = nest_utils.prune_extra_keys(self._time_step_spec,
time_step)
policy_state = nest_utils.prune_extra_keys(self._policy_state_spec,
policy_state)
nest_utils.assert_same_structure(
time_step,
self._time_step_spec,
message='time_step and time_step_spec structures do not match')
nest_utils.assert_same_structure(
policy_state,
self._policy_state_spec,
message=
'policy_state and policy_state_spec structures do not match')
if self._automatic_state_reset:
policy_state = self._maybe_reset_state(time_step, policy_state)
step = self._distribution(time_step=time_step,
policy_state=policy_state)
if self.emit_log_probability:
# This here is set only for compatibility with info_spec in constructor.
info = policy_step.set_log_probability(
step.info,
tf.nest.map_structure(
lambda _: tf.constant(0., dtype=tf.float32),
policy_step.get_log_probability(self._info_spec)))
step = step._replace(info=info)
if self._validate_args:
nest_utils.assert_same_structure(
step,
self._policy_step_spec,
message=('distribution output and policy_step_spec structures '
'do not match'))
return step
def _assert_nested_variable_updated(
self,
variables: types.NestedVariable,
check_nest_seq_types: bool = True) -> None:
# Prepare the exptected content of the variables.
expected_values = (tf.constant(0, dtype=tf.int64, shape=()), {
'var1': (tf.constant([1, 1], dtype=tf.float64, shape=(2,)),),
'var2': tf.constant([[2], [3]], dtype=tf.int32, shape=(2, 1))
})
flat_expected_values = tf.nest.flatten(expected_values)
# Assert that the variables have the same content as the expected values.
# Meaning that the two nested structure have to be the same.
self.assertIsNone(
nest_utils.assert_same_structure(
variables, expected_values, check_types=check_nest_seq_types))
# And the values in `variables` have to be equal to (or close to, depending
# on the component type) to the expected ones.
flat_variables = tf.nest.flatten(variables)
self.assertAllEqual(flat_variables[0], flat_expected_values[0])
self.assertAllClose(flat_variables[1], flat_expected_values[1])
self.assertAllEqual(flat_variables[2], flat_expected_values[2])
def critic_no_entropy_loss(self,
time_steps,
actions,
next_time_steps,
td_errors_loss_fn,
gamma=1.0,
reward_scale_factor=1.0,
weights=None,
training=False):
"""Computes the critic loss for SAC training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for future rewards.
reward_scale_factor: Multiplicative factor to scale rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
Returns:
critic_loss: A scalar critic loss.
"""
with tf.name_scope('critic_no_entropy_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps,
self.time_step_spec)
next_actions, _ = self._actions_and_log_probs(next_time_steps)
target_input = (next_time_steps.observation, next_actions)
target_q_values1, unused_network_state1 = self._target_critic_network_no_entropy_1(
target_input, next_time_steps.step_type, training=False)
target_q_values2, unused_network_state2 = self._target_critic_network_no_entropy_2(
target_input, next_time_steps.step_type, training=False)
target_q_values = tf.minimum(
target_q_values1, target_q_values2
) # entropy has been removed from the target critic function
td_targets = tf.stop_gradient(
reward_scale_factor * next_time_steps.reward +
gamma * next_time_steps.discount * target_q_values)
pred_input = (time_steps.observation, actions)
pred_td_targets1, _ = self._critic_network_no_entropy_1(
pred_input, time_steps.step_type, training=training)
pred_td_targets2, _ = self._critic_network_no_entropy_2(
pred_input, time_steps.step_type, training=training)
critic_loss1 = td_errors_loss_fn(td_targets, pred_td_targets1)
critic_loss2 = td_errors_loss_fn(td_targets, pred_td_targets2)
critic_loss = critic_loss1 + critic_loss2
if nest_utils.is_batched_nested_tensors(time_steps,
self.time_step_spec,
num_outer_dims=2):
# Sum over the time dimension.
critic_loss = tf.reduce_sum(input_tensor=critic_loss, axis=1)
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
sample_weight=weights,
regularization_loss=(self._critic_network_no_entropy_1.losses +
self._critic_network_no_entropy_2.losses))
critic_no_entropy_loss = agg_loss.total_loss
self._critic_no_entropy_loss_debug_summaries(
td_targets, pred_td_targets1, pred_td_targets2)
return critic_no_entropy_loss
def action(self, time_step, policy_state=(), seed=None):
"""Generates next action given the time_step and policy_state.
Args:
time_step: A `TimeStep` tuple corresponding to `time_step_spec()`.
policy_state: A Tensor, or a nested dict, list or tuple of Tensors
representing the previous policy_state.
seed: Seed to use if action performs sampling (optional).
Returns:
A `PolicyStep` named tuple containing:
`action`: An action Tensor matching the `action_spec`.
`state`: A policy state tensor to be fed into the next call to action.
`info`: Optional side information such as action log probabilities.
Raises:
RuntimeError: If subclass __init__ didn't call super().__init__.
ValueError or TypeError: If `validate_args is True` and inputs or
outputs do not match `time_step_spec`, `policy_state_spec`,
or `policy_step_spec`.
"""
if self._enable_functions and getattr(self, '_action_fn',
None) is None:
raise RuntimeError(
'Cannot find _action_fn. Did %s.__init__ call super?' %
type(self).__name__)
if self._enable_functions:
action_fn = self._action_fn
else:
action_fn = self._action
if self._validate_args:
time_step = nest_utils.prune_extra_keys(self._time_step_spec,
time_step)
policy_state = nest_utils.prune_extra_keys(self._policy_state_spec,
policy_state)
nest_utils.assert_same_structure(
time_step,
self._time_step_spec,
message='time_step and time_step_spec structures do not match')
nest_utils.assert_same_structure(
policy_state,
self._policy_state_spec,
message=
'policy_state and policy_state_spec structures do not match')
if self._automatic_state_reset:
policy_state = self._maybe_reset_state(time_step, policy_state)
step = action_fn(time_step=time_step,
policy_state=policy_state,
seed=seed)
def clip_action(action, action_spec):
if isinstance(action_spec, tensor_spec.BoundedTensorSpec):
return common.clip_to_spec(action, action_spec)
return action
if self._validate_args:
nest_utils.assert_same_structure(
step.action,
self._action_spec,
message='action and action_spec structures do not match')
if self._clip:
clipped_actions = tf.nest.map_structure(clip_action, step.action,
self._action_spec)
step = step._replace(action=clipped_actions)
if self._validate_args:
nest_utils.assert_same_structure(
step,
self._policy_step_spec,
message=
'action output and policy_step_spec structures do not match')
def compare_to_spec(value, spec):
return value.dtype.is_compatible_with(spec.dtype)
compatibility = [
compare_to_spec(v, s)
for (v, s) in zip(tf.nest.flatten(step.action),
tf.nest.flatten(self.action_spec))
]
if not all(compatibility):
get_dtype = lambda x: x.dtype
action_dtypes = tf.nest.map_structure(get_dtype, step.action)
spec_dtypes = tf.nest.map_structure(get_dtype,
self.action_spec)
raise TypeError(
'Policy produced an action with a dtype that doesn\'t '
'match its action_spec. Got action:\n %s\n with '
'action_spec:\n %s' % (action_dtypes, spec_dtypes))
return step
def as_dict(outputs, output_spec):
nest_utils.assert_same_structure(outputs, output_spec)
flat_outputs = tf.nest.flatten(outputs)
flat_names = [s.name for s in tf.nest.flatten(output_spec)]
return dict(zip(flat_names, flat_outputs))
def _loss(self,
experience,
td_errors_loss_fn=tf.compat.v1.losses.huber_loss,
gamma=1.0,
reward_scale_factor=1.0,
weights=None,
training=False):
"""Computes critic loss for CategoricalDQN training.
See Algorithm 1 and the discussion immediately preceding it in page 6 of
"A Distributional Perspective on Reinforcement Learning"
Bellemare et al., 2017
https://arxiv.org/abs/1707.06887
Args:
experience: A batch of experience data in the form of a `Trajectory`. The
structure of `experience` must match that of `self.policy.step_spec`.
All tensors in `experience` must be shaped `[batch, time, ...]` where
`time` must be equal to `self.required_experience_time_steps` if that
property is not `None`.
td_errors_loss_fn: A function(td_targets, predictions) to compute loss.
gamma: Discount for future rewards.
reward_scale_factor: Multiplicative factor to scale rewards.
weights: Optional weights used for importance sampling.
training: Whether the loss is being used for training.
Returns:
critic_loss: A scalar critic loss.
Raises:
ValueError:
if the number of actions is greater than 1.
"""
# Check that `experience` includes two outer dimensions [B, T, ...]. This
# method requires a time dimension to compute the loss properly.
self._check_trajectory_dimensions(experience)
squeeze_time_dim = not self._q_network.state_spec
if self._n_step_update == 1:
time_steps, policy_steps, next_time_steps = (
trajectory.experience_to_transitions(experience,
squeeze_time_dim))
actions = policy_steps.action
else:
# To compute n-step returns, we need the first time steps, the first
# actions, and the last time steps. Therefore we extract the first and
# last transitions from our Trajectory.
first_two_steps = tf.nest.map_structure(lambda x: x[:, :2],
experience)
last_two_steps = tf.nest.map_structure(lambda x: x[:, -2:],
experience)
time_steps, policy_steps, _ = (
trajectory.experience_to_transitions(first_two_steps,
squeeze_time_dim))
actions = policy_steps.action
_, _, next_time_steps = (trajectory.experience_to_transitions(
last_two_steps, squeeze_time_dim))
with tf.name_scope('critic_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps,
self.time_step_spec)
rank = nest_utils.get_outer_rank(time_steps.observation,
self._time_step_spec.observation)
# If inputs have a time dimension and the q_network is stateful,
# combine the batch and time dimension.
batch_squash = (None if rank <= 1 or self._q_network.state_spec
in ((), None) else network_utils.BatchSquash(rank))
network_observation = time_steps.observation
if self._observation_and_action_constraint_splitter is not None:
network_observation, _ = (
self._observation_and_action_constraint_splitter(
network_observation))
# q_logits contains the Q-value logits for all actions.
q_logits, _ = self._q_network(network_observation,
time_steps.step_type,
training=training)
if batch_squash is not None:
# Squash outer dimensions to a single dimensions for facilitation
# computing the loss the following. Required for supporting temporal
# inputs, for example.
q_logits = batch_squash.flatten(q_logits)
actions = batch_squash.flatten(actions)
next_time_steps = tf.nest.map_structure(
batch_squash.flatten, next_time_steps)
next_q_distribution = self._next_q_distribution(next_time_steps)
if actions.shape.rank > 1:
actions = tf.squeeze(actions,
list(range(1, actions.shape.rank)))
# Project the sample Bellman update \hat{T}Z_{\theta} onto the original
# support of Z_{\theta} (see Figure 1 in paper).
batch_size = q_logits.shape[0] or tf.shape(q_logits)[0]
tiled_support = tf.tile(self._support, [batch_size])
tiled_support = tf.reshape(tiled_support,
[batch_size, self._num_atoms])
if self._n_step_update == 1:
discount = next_time_steps.discount
if discount.shape.rank == 1:
# We expect discount to have a shape of [batch_size], while
# tiled_support will have a shape of [batch_size, num_atoms]. To
# multiply these, we add a second dimension of 1 to the discount.
discount = tf.expand_dims(discount, -1)
next_value_term = tf.multiply(discount,
tiled_support,
name='next_value_term')
reward = next_time_steps.reward
if reward.shape.rank == 1:
# See the explanation above.
reward = tf.expand_dims(reward, -1)
reward_term = tf.multiply(reward_scale_factor,
reward,
name='reward_term')
target_support = tf.add(reward_term,
gamma * next_value_term,
name='target_support')
else:
# When computing discounted return, we need to throw out the last time
# index of both reward and discount, which are filled with dummy values
# to match the dimensions of the observation.
rewards = reward_scale_factor * experience.reward[:, :-1]
discounts = gamma * experience.discount[:, :-1]
# TODO(b/134618876): Properly handle Trajectories that include episode
# boundaries with nonzero discount.
discounted_returns = value_ops.discounted_return(
rewards=rewards,
discounts=discounts,
final_value=tf.zeros([batch_size], dtype=discounts.dtype),
time_major=False,
provide_all_returns=False)
# Convert discounted_returns from [batch_size] to [batch_size, 1]
discounted_returns = tf.expand_dims(discounted_returns, -1)
final_value_discount = tf.reduce_prod(discounts, axis=1)
final_value_discount = tf.expand_dims(final_value_discount, -1)
# Save the values of discounted_returns and final_value_discount in
# order to check them in unit tests.
self._discounted_returns = discounted_returns
self._final_value_discount = final_value_discount
target_support = tf.add(discounted_returns,
final_value_discount * tiled_support,
name='target_support')
target_distribution = tf.stop_gradient(
project_distribution(target_support, next_q_distribution,
self._support))
# Obtain the current Q-value logits for the selected actions.
indices = tf.range(batch_size)
indices = tf.cast(indices, actions.dtype)
reshaped_actions = tf.stack([indices, actions], axis=-1)
chosen_action_logits = tf.gather_nd(q_logits, reshaped_actions)
# Compute the cross-entropy loss between the logits. If inputs have
# a time dimension, compute the sum over the time dimension before
# computing the mean over the batch dimension.
if batch_squash is not None:
target_distribution = batch_squash.unflatten(
target_distribution)
chosen_action_logits = batch_squash.unflatten(
chosen_action_logits)
critic_loss = tf.reduce_sum(
tf.compat.v1.nn.softmax_cross_entropy_with_logits_v2(
labels=target_distribution,
logits=chosen_action_logits),
axis=1)
else:
critic_loss = tf.compat.v1.nn.softmax_cross_entropy_with_logits_v2(
labels=target_distribution, logits=chosen_action_logits)
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
regularization_loss=self._q_network.losses)
total_loss = agg_loss.total_loss
dict_losses = {
'critic_loss': agg_loss.weighted,
'reg_loss': agg_loss.regularization,
'total_loss': total_loss
}
common.summarize_scalar_dict(dict_losses,
step=self.train_step_counter,
name_scope='Losses/')
if self._debug_summaries:
distribution_errors = target_distribution - chosen_action_logits
with tf.name_scope('distribution_errors'):
common.generate_tensor_summaries(
'distribution_errors',
distribution_errors,
step=self.train_step_counter)
tf.compat.v2.summary.scalar(
'mean',
tf.reduce_mean(distribution_errors),
step=self.train_step_counter)
tf.compat.v2.summary.scalar(
'mean_abs',
tf.reduce_mean(tf.abs(distribution_errors)),
step=self.train_step_counter)
tf.compat.v2.summary.scalar(
'max',
tf.reduce_max(distribution_errors),
step=self.train_step_counter)
tf.compat.v2.summary.scalar(
'min',
tf.reduce_min(distribution_errors),
step=self.train_step_counter)
with tf.name_scope('target_distribution'):
common.generate_tensor_summaries(
'target_distribution',
target_distribution,
step=self.train_step_counter)
# TODO(b/127318640): Give appropriate values for td_loss and td_error for
# prioritized replay.
return tf_agent.LossInfo(
total_loss, dqn_agent.DqnLossInfo(td_loss=(), td_error=()))
def critic_loss(self,
time_steps,
actions,
next_time_steps,
augmented_obs,
augmented_next_obs,
td_errors_loss_fn,
gamma=1.0,
reward_scale_factor=1.0,
weights=None,
training=False):
"""Computes the critic loss for SAC training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
augmented_obs: List of observations.
augmented_next_obs: List of next_observations.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for future rewards.
reward_scale_factor: Multiplicative factor to scale rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
Returns:
critic_loss: A scalar critic loss.
"""
with tf.name_scope('critic_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps, self.time_step_spec)
td_targets = self._compute_td_targets(next_time_steps,
reward_scale_factor, gamma)
# Compute td_targets with augmentations.
for i in range(self._num_augmentations - 1):
augmented_next_time_steps = next_time_steps._replace(
observation=augmented_next_obs[i])
augmented_td_targets = self._compute_td_targets(
augmented_next_time_steps, reward_scale_factor, gamma)
td_targets = td_targets + augmented_td_targets
# Average td_target estimation over augmentations.
if self._num_augmentations > 1:
td_targets = td_targets / self._num_augmentations
pred_td_targets1, pred_td_targets2, critic_loss = (
self._compute_prediction_critic_loss(
(time_steps.observation, actions), td_targets, time_steps,
training, td_errors_loss_fn))
# Add Q Augmentations to the critic loss.
for i in range(self._num_augmentations - 1):
augmented_time_steps = time_steps._replace(observation=augmented_obs[i])
_, _, loss = (
self._compute_prediction_critic_loss(
(augmented_time_steps.observation, actions), td_targets,
augmented_time_steps, training, td_errors_loss_fn))
critic_loss = critic_loss + loss
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
sample_weight=weights,
regularization_loss=(self._critic_network_1.losses +
self._critic_network_2.losses))
critic_loss = agg_loss.total_loss
self._critic_loss_debug_summaries(td_targets, pred_td_targets1,
pred_td_targets2)
return critic_loss
def critic_loss(self,
time_steps,
expert_experience,
actions,
next_time_steps,
future_time_steps,
td_errors_loss_fn,
gamma = 1.0,
reward_scale_factor = 1.0,
weights = None,
training = False,
loss_name='c',
use_done=False,
q_combinator='min'):
"""Computes the critic loss for SAC training.
Args:
time_steps: A batch of timesteps.
expert_experience: An array of success examples.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
future_time_steps: A batch of future timesteps, used for n-step returns.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for future rewards.
reward_scale_factor: Multiplicative factor to scale rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
loss_name: Which loss function to use. Use 'c' for RCE and 'q' for SQIL.
use_done: Whether to use the terminal flag from the environment in the
Bellman backup. We found that omitting it led to better results.
q_combinator: Whether to combine the two Q-functions by taking the 'min'
(as in TD3) or the 'max'.
Returns:
critic_loss: A scalar critic loss.
"""
assert weights is None
with tf.name_scope('critic_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps, self.time_step_spec)
next_actions, _ = self._actions_and_log_probs(next_time_steps)
target_input = (next_time_steps.observation, next_actions)
target_q_values1, unused_network_state1 = self._target_critic_network_1(
target_input, next_time_steps.step_type, training=False)
target_q_values2, unused_network_state2 = self._target_critic_network_2(
target_input, next_time_steps.step_type, training=False)
if self._n_step is not None:
future_actions, _ = self._actions_and_log_probs(future_time_steps)
future_input = (future_time_steps.observation, future_actions)
future_q_values1, _ = self._target_critic_network_1(
future_input, future_time_steps.step_type, training=False)
future_q_values2, _ = self._target_critic_network_2(
future_input, future_time_steps.step_type, training=False)
gamma_n = gamma**self._n_step # Discount for n-step returns
target_q_values1 = (target_q_values1 + gamma_n * future_q_values1) / 2.0
target_q_values2 = (target_q_values2 + gamma_n * future_q_values2) / 2.0
if q_combinator == 'min':
target_q_values = tf.minimum(target_q_values1, target_q_values2)
else:
assert q_combinator == 'max'
target_q_values = tf.maximum(target_q_values1, target_q_values2)
batch_size = time_steps.observation.shape[0]
if loss_name == 'q':
if use_done:
td_targets = gamma * next_time_steps.discount * target_q_values
else:
td_targets = gamma * target_q_values
else:
assert loss_name == 'c'
w = target_q_values / (1 - target_q_values)
td_targets = gamma * w / (gamma * w + 1)
if use_done:
td_targets = next_time_steps.discount * td_targets
weights = tf.concat([1 + gamma * w, (1 - gamma) * tf.ones(batch_size)],
axis=0)
td_targets = tf.stop_gradient(td_targets)
td_targets = tf.concat([td_targets, tf.ones(batch_size)], axis=0)
# Note that the actions only depend on the observations. We create the
# expert_time_steps object simply to make this look like a time step
# object.
expert_time_steps = time_steps._replace(observation=expert_experience)
if self._use_behavior_policy:
policy_state = self._train_policy.get_initial_state(batch_size)
action_distribution = self._behavior_policy.distribution(
time_steps, policy_state=policy_state).action
# Sample actions and log_pis from transformed distribution.
expert_actions = tf.nest.map_structure(lambda d: d.sample(),
action_distribution)
else:
expert_actions, _ = self._actions_and_log_probs(expert_time_steps)
observation = time_steps.observation
pred_input = (tf.concat([observation, expert_experience], axis=0),
tf.concat([actions, expert_actions], axis=0))
pred_td_targets1, _ = self._critic_network_1(
pred_input, time_steps.step_type, training=training)
pred_td_targets2, _ = self._critic_network_2(
pred_input, time_steps.step_type, training=training)
self._critic_loss_debug_summaries(td_targets, pred_td_targets1,
pred_td_targets2)
critic_loss1 = td_errors_loss_fn(td_targets, pred_td_targets1)
critic_loss2 = td_errors_loss_fn(td_targets, pred_td_targets2)
critic_loss = critic_loss1 + critic_loss2
if critic_loss.shape.rank > 1:
# Sum over the time dimension.
critic_loss = tf.reduce_sum(
critic_loss, axis=range(1, critic_loss.shape.rank))
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
sample_weight=weights,
regularization_loss=(self._critic_network_1.losses +
self._critic_network_2.losses))
critic_loss = agg_loss.total_loss
self._critic_loss_debug_summaries(td_targets, pred_td_targets1,
pred_td_targets2)
return critic_loss
def run(self, trajectory, policy_state=None):
"""Apply the policy to trajectory steps and store actions/info.
If `self.time_major == True`, the tensors in `trajectory` are assumed to
have shape `[time, batch, ...]`. Otherwise they are assumed to
have shape `[batch, time, ...]`.
Args:
trajectory: The `Trajectory` to run against.
If the replay class was created with `time_major=True`, then
the tensors in trajectory must be shaped `[time, batch, ...]`.
Otherwise they must be shaped `[batch, time, ...]`.
policy_state: (optional) A nest Tensor with initial step policy state.
Returns:
output_actions: A nest of the actions that the policy took.
If the replay class was created with `time_major=True`, then
the tensors here will be shaped `[time, batch, ...]`. Otherwise
they'll be shaped `[batch, time, ...]`.
output_policy_info: A nest of the policy info that the policy emitted.
If the replay class was created with `time_major=True`, then
the tensors here will be shaped `[time, batch, ...]`. Otherwise
they'll be shaped `[batch, time, ...]`.
policy_state: A nest Tensor with final step policy state.
Raises:
TypeError: If `policy_state` structure doesn't match
`self.policy.policy_state_spec`, or `trajectory` structure doesn't
match `self.policy.trajectory_spec`.
ValueError: If `policy_state` doesn't match
`self.policy.policy_state_spec`, or `trajectory` structure doesn't
match `self.policy.trajectory_spec`.
ValueError: If `trajectory` lacks two outer dims.
"""
trajectory_spec = self._policy.trajectory_spec
outer_dims = nest_utils.get_outer_shape(trajectory, trajectory_spec)
if tf.compat.dimension_value(outer_dims.shape[0]) != 2:
raise ValueError(
"Expected two outer dimensions, but saw '{}' dimensions.\n"
"Trajectory:\n{}.\nTrajectory spec from policy:\n{}.".format(
tf.compat.dimension_value(outer_dims.shape[0]), trajectory,
trajectory_spec))
if self._time_major:
sequence_length = outer_dims[0]
batch_size = outer_dims[1]
static_batch_size = tf.compat.dimension_value(
trajectory.discount.shape[1])
else:
batch_size = outer_dims[0]
sequence_length = outer_dims[1]
static_batch_size = tf.compat.dimension_value(
trajectory.discount.shape[0])
if policy_state is None:
policy_state = self._policy.get_initial_state(batch_size)
else:
nest_utils.assert_same_structure(policy_state,
self._policy.policy_state_spec)
if not self._time_major:
# Make trajectory time-major.
trajectory = tf.nest.map_structure(common.transpose_batch_time,
trajectory)
trajectory_tas = tf.nest.map_structure(
lambda t: tf.TensorArray(t.dtype, size=sequence_length).unstack(t),
trajectory)
def create_output_ta(spec):
return tf.TensorArray(spec.dtype,
size=sequence_length,
element_shape=(tf.TensorShape([
static_batch_size
]).concatenate(spec.shape)))
output_action_tas = tf.nest.map_structure(create_output_ta,
trajectory_spec.action)
output_policy_info_tas = tf.nest.map_structure(
create_output_ta, trajectory_spec.policy_info)
read0 = lambda ta: ta.read(0)
zeros_like0 = lambda t: tf.zeros_like(t[0])
ones_like0 = lambda t: tf.ones_like(t[0])
time_step = ts.TimeStep(
step_type=read0(trajectory_tas.step_type),
reward=tf.nest.map_structure(zeros_like0, trajectory.reward),
discount=ones_like0(trajectory.discount),
observation=tf.nest.map_structure(read0,
trajectory_tas.observation))
def process_step(time, time_step, policy_state, output_action_tas,
output_policy_info_tas):
"""Take an action on the given step, and update output TensorArrays.
Args:
time: Step time. Describes which row to read from the trajectory
TensorArrays and which location to write into in the output
TensorArrays.
time_step: Previous step's `TimeStep`.
policy_state: Policy state tensor or nested structure of tensors.
output_action_tas: Nest of `tf.TensorArray` containing new actions.
output_policy_info_tas: Nest of `tf.TensorArray` containing new
policy info.
Returns:
policy_state: The next policy state.
next_output_action_tas: Updated `output_action_tas`.
next_output_policy_info_tas: Updated `output_policy_info_tas`.
"""
action_step = self._policy.action(time_step, policy_state)
policy_state = action_step.state
write_ta = lambda ta, t: ta.write(time - 1, t)
next_output_action_tas = tf.nest.map_structure(
write_ta, output_action_tas, action_step.action)
next_output_policy_info_tas = tf.nest.map_structure(
write_ta, output_policy_info_tas, action_step.info)
return (action_step.state, next_output_action_tas,
next_output_policy_info_tas)
def loop_body(time, time_step, policy_state, output_action_tas,
output_policy_info_tas):
"""Runs a step in environment.
While loop will call multiple times.
Args:
time: Step time.
time_step: Previous step's `TimeStep`.
policy_state: Policy state tensor or nested structure of tensors.
output_action_tas: Updated nest of `tf.TensorArray`, the new actions.
output_policy_info_tas: Updated nest of `tf.TensorArray`, the new
policy info.
Returns:
loop_vars for next iteration of tf.while_loop.
"""
policy_state, next_output_action_tas, next_output_policy_info_tas = (
process_step(time, time_step, policy_state, output_action_tas,
output_policy_info_tas))
ta_read = lambda ta: ta.read(time)
ta_read_prev = lambda ta: ta.read(time - 1)
time_step = ts.TimeStep(
step_type=ta_read(trajectory_tas.step_type),
observation=tf.nest.map_structure(ta_read,
trajectory_tas.observation),
reward=tf.nest.map_structure(ta_read_prev,
trajectory_tas.reward),
discount=ta_read_prev(trajectory_tas.discount))
return (time + 1, time_step, policy_state, next_output_action_tas,
next_output_policy_info_tas)
time = tf.constant(1)
time, time_step, policy_state, output_action_tas, output_policy_info_tas = (
tf.while_loop(cond=lambda time, *_: time < sequence_length,
body=loop_body,
loop_vars=[
time, time_step, policy_state, output_action_tas,
output_policy_info_tas
],
back_prop=False,
name="trajectory_replay_loop"))
# Run the last time step
last_policy_state, output_action_tas, output_policy_info_tas = (
process_step(time, time_step, policy_state, output_action_tas,
output_policy_info_tas))
def stack_ta(ta):
t = ta.stack()
if not self._time_major:
t = common.transpose_batch_time(t)
return t
stacked_output_actions = tf.nest.map_structure(stack_ta,
output_action_tas)
stacked_output_policy_info = tf.nest.map_structure(
stack_ta, output_policy_info_tas)
return (stacked_output_actions, stacked_output_policy_info,
last_policy_state)
def critic_loss(self,
time_steps: ts.TimeStep,
actions: types.Tensor,
next_time_steps: ts.TimeStep,
td_errors_loss_fn: types.LossFn,
gamma: types.Float = 1.0,
reward_scale_factor: types.Float = 1.0,
weights: Optional[types.Tensor] = None,
training: bool = False) -> types.Tensor:
"""Computes the critic loss for SAC training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for future rewards.
reward_scale_factor: Multiplicative factor to scale rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
Returns:
critic_loss: A scalar critic loss.
"""
with tf.name_scope('critic_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps,
self.time_step_spec)
next_actions, next_log_pis = self._actions_and_log_probs(
next_time_steps)
target_input = (next_time_steps.observation, next_actions)
target_q_values1, unused_network_state1 = self._target_critic_network_1(
target_input, next_time_steps.step_type, training=False)
target_q_values2, unused_network_state2 = self._target_critic_network_2(
target_input, next_time_steps.step_type, training=False)
target_q_values = (tf.minimum(target_q_values1, target_q_values2) -
tf.exp(self._log_alpha) * next_log_pis)
td_targets = tf.stop_gradient(
reward_scale_factor * next_time_steps.reward +
gamma * next_time_steps.discount * target_q_values)
pred_input = (time_steps.observation, actions)
pred_td_targets1, _ = self._critic_network_1(pred_input,
time_steps.step_type,
training=training)
pred_td_targets2, _ = self._critic_network_2(pred_input,
time_steps.step_type,
training=training)
critic_loss1 = td_errors_loss_fn(td_targets, pred_td_targets1)
critic_loss2 = td_errors_loss_fn(td_targets, pred_td_targets2)
critic_loss = critic_loss1 + critic_loss2
if critic_loss.shape.rank > 1:
# Sum over the time dimension.
critic_loss = tf.reduce_sum(critic_loss,
axis=range(1,
critic_loss.shape.rank))
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
sample_weight=weights,
regularization_loss=(self._critic_network_1.losses +
self._critic_network_2.losses))
critic_loss = agg_loss.total_loss
self._critic_loss_debug_summaries(td_targets, pred_td_targets1,
pred_td_targets2)
return critic_loss
def total_loss(self,
experience: traj.Trajectory,
returns: types.Tensor,
weights: types.Tensor,
training: bool = False) -> tf_agent.LossInfo:
# Ensure we see at least one full episode.
time_steps = ts.TimeStep(experience.step_type,
tf.zeros_like(experience.reward),
tf.zeros_like(experience.discount),
experience.observation)
is_last = experience.is_last()
num_episodes = tf.reduce_sum(tf.cast(is_last, tf.float32))
tf.debugging.assert_greater(
num_episodes,
0.0,
message=
'No complete episode found. REINFORCE requires full episodes '
'to compute losses.')
# Mask out partial episodes at the end of each batch of time_steps.
# NOTE: We use is_last rather than is_boundary because the last transition
# is the transition with the last valid reward. In other words, the
# reward on the boundary transitions do not have valid rewards. Since
# REINFORCE is calculating a loss w.r.t. the returns (and not bootstrapping)
# keeping the boundary transitions is irrelevant.
valid_mask = tf.cast(experience.is_last(), dtype=tf.float32)
valid_mask = tf.math.cumsum(valid_mask, axis=1, reverse=True)
valid_mask = tf.cast(valid_mask > 0, dtype=tf.float32)
if weights is not None:
weights *= valid_mask
else:
weights = valid_mask
advantages = returns
value_preds = None
if self._baseline:
value_preds, _ = self._value_network(time_steps.observation,
time_steps.step_type,
training=True)
if self._debug_summaries:
tf.compat.v2.summary.histogram(name='value_preds',
data=value_preds,
step=self.train_step_counter)
advantages = self._advantage_fn(returns, value_preds)
if self._debug_summaries:
tf.compat.v2.summary.histogram(name='advantages',
data=advantages,
step=self.train_step_counter)
# TODO(b/126592060): replace with tensor normalizer.
if self._normalize_returns:
advantages = _standard_normalize(advantages, axes=(0, 1))
if self._debug_summaries:
tf.compat.v2.summary.histogram(
name='normalized_%s' %
('advantages' if self._baseline else 'returns'),
data=advantages,
step=self.train_step_counter)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
policy_state = _get_initial_policy_state(self.collect_policy,
time_steps)
actions_distribution = self.collect_policy.distribution(
time_steps, policy_state=policy_state).action
policy_gradient_loss = self.policy_gradient_loss(
actions_distribution,
experience.action,
experience.is_boundary(),
advantages,
num_episodes,
weights,
)
entropy_regularization_loss = self.entropy_regularization_loss(
actions_distribution, weights)
network_regularization_loss = tf.nn.scale_regularization_loss(
self._actor_network.losses)
total_loss = (policy_gradient_loss + network_regularization_loss +
entropy_regularization_loss)
losses_dict = {
'policy_gradient_loss': policy_gradient_loss,
'policy_network_regularization_loss': network_regularization_loss,
'entropy_regularization_loss': entropy_regularization_loss,
'value_estimation_loss': 0.0,
'value_network_regularization_loss': 0.0,
}
value_estimation_loss = None
if self._baseline:
value_estimation_loss = self.value_estimation_loss(
value_preds, returns, num_episodes, weights)
value_network_regularization_loss = tf.nn.scale_regularization_loss(
self._value_network.losses)
total_loss += value_estimation_loss + value_network_regularization_loss
losses_dict['value_estimation_loss'] = value_estimation_loss
losses_dict['value_network_regularization_loss'] = (
value_network_regularization_loss)
loss_info_extra = ReinforceAgentLossInfo(**losses_dict)
losses_dict[
'total_loss'] = total_loss # Total loss not in loss_info_extra.
common.summarize_scalar_dict(losses_dict,
self.train_step_counter,
name_scope='Losses/')
return tf_agent.LossInfo(total_loss, loss_info_extra)
def critic_loss(
self,
time_steps,
actions,
next_time_steps,
td_errors_loss_fn,
gamma=1.0,
weights=None,
training=False,
w_clipping=None,
self_normalized=False,
lambda_fix=False,
):
"""Computes the critic loss for C-learning training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for future rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
w_clipping: Maximum value used for clipping the weights. Use -1 to do no
clipping; use None to use the recommended value of 1 / (1 - gamma).
self_normalized: Whether to normalize the weights to the average is 1.
Empirically this usually hurts performance.
lambda_fix: Whether to include the adjustment when using future positives.
Empirically this has little effect.
Returns:
critic_loss: A scalar critic loss.
"""
del weights
if w_clipping is None:
w_clipping = 1 / (1 - gamma)
rfp = gin.query_parameter('goal_fn.relabel_future_prob')
rnp = gin.query_parameter('goal_fn.relabel_next_prob')
assert rfp + rnp == 0.5
with tf.name_scope('critic_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps,
self.time_step_spec)
next_actions, _ = self._actions_and_log_probs(next_time_steps)
target_input = (next_time_steps.observation, next_actions)
target_q_values1, unused_network_state1 = self._target_critic_network_1(
target_input, next_time_steps.step_type, training=False)
target_q_values2, unused_network_state2 = self._target_critic_network_2(
target_input, next_time_steps.step_type, training=False)
target_q_values = tf.minimum(target_q_values1, target_q_values2)
w = tf.stop_gradient(target_q_values / (1 - target_q_values))
if w_clipping >= 0:
w = tf.clip_by_value(w, 0, w_clipping)
tf.debugging.assert_all_finite(w,
'Not all elements of w are finite')
if self_normalized:
w = w / tf.reduce_mean(w)
batch_size = nest_utils.get_outer_shape(time_steps,
self._time_step_spec)[0]
half_batch = batch_size // 2
float_batch_size = tf.cast(batch_size, float)
num_next = tf.cast(tf.round(float_batch_size * rnp), tf.int32)
num_future = tf.cast(tf.round(float_batch_size * rfp), tf.int32)
if lambda_fix:
lambda_coef = 2 * rnp
weights = tf.concat([
tf.fill((num_next, ), (1 - gamma)),
tf.fill((num_future, ), 1.0),
(1 + lambda_coef * gamma * w)[half_batch:]
],
axis=0)
else:
weights = tf.concat([
tf.fill((num_next, ), (1 - gamma)),
tf.fill((num_future, ), 1.0), (1 + gamma * w)[half_batch:]
],
axis=0)
# Note that we assume that episodes never terminate. If they do, then
# we need to include next_time_steps.discount in the (negative) TD target.
# We exclude the termination here so that we can use termination to
# indicate task success during evaluation. In the evaluation setting,
# task success depends on the task, but we don't want the termination
# here to depend on the task. Hence, we ignored it.
if lambda_fix:
lambda_coef = 2 * rnp
y = lambda_coef * gamma * w / (1 + lambda_coef * gamma * w)
else:
y = gamma * w / (1 + gamma * w)
td_targets = tf.stop_gradient(next_time_steps.reward +
(1 - next_time_steps.reward) * y)
if rfp > 0:
td_targets = tf.concat(
[tf.ones(half_batch), td_targets[half_batch:]], axis=0)
observation = time_steps.observation
pred_input = (observation, actions)
pred_td_targets1, _ = self._critic_network_1(pred_input,
time_steps.step_type,
training=training)
pred_td_targets2, _ = self._critic_network_2(pred_input,
time_steps.step_type,
training=training)
critic_loss1 = td_errors_loss_fn(td_targets, pred_td_targets1)
critic_loss2 = td_errors_loss_fn(td_targets, pred_td_targets2)
critic_loss = critic_loss1 + critic_loss2
if critic_loss.shape.rank > 1:
# Sum over the time dimension.
critic_loss = tf.reduce_sum(critic_loss,
axis=range(1,
critic_loss.shape.rank))
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
sample_weight=weights,
regularization_loss=(self._critic_network_1.losses +
self._critic_network_2.losses))
critic_loss = agg_loss.total_loss
self._critic_loss_debug_summaries(td_targets, pred_td_targets1,
pred_td_targets2, weights)
return critic_loss
def _critic_loss_with_optional_entropy_term(
self,
time_steps: ts.TimeStep,
actions: types.Tensor,
next_time_steps: ts.TimeStep,
td_errors_loss_fn: types.LossFn,
gamma: types.Float = 1.0,
reward_scale_factor: types.Float = 1.0,
weights: Optional[types.Tensor] = None,
training: bool = False) -> types.Tensor:
r"""Computes the critic loss for CQL-SAC training.
The original SAC critic loss is:
```
(q(s, a) - (r(s, a) + \gamma q(s', a') - \gamma \alpha \log \pi(a'|s')))^2
```
The CQL-SAC critic loss makes the entropy term optional.
CQL may value unseen actions higher since it lower-bounds the value of
seen actions. This makes the policy entropy potentially redundant in the
target term, since it will further enhance unseen actions' effects.
If self._include_critic_entropy_term is False, this loss equation becomes:
```
(q(s, a) - (r(s, a) + \gamma q(s', a')))^2
```
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for future rewards.
reward_scale_factor: Multiplicative factor to scale rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
Returns:
critic_loss: A scalar critic loss.
"""
with tf.name_scope('critic_loss'):
nest_utils.assert_same_structure(actions, self.action_spec)
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
nest_utils.assert_same_structure(next_time_steps,
self.time_step_spec)
# We do not update actor or target networks in critic loss.
next_actions, next_log_pis = self._actions_and_log_probs(
next_time_steps, training=False)
target_input = (next_time_steps.observation, next_actions)
target_q_values1, unused_network_state1 = self._target_critic_network_1(
target_input, next_time_steps.step_type, training=False)
target_q_values2, unused_network_state2 = self._target_critic_network_2(
target_input, next_time_steps.step_type, training=False)
target_q_values = tf.minimum(target_q_values1, target_q_values2)
if self._include_critic_entropy_term:
target_q_values -= (tf.exp(self._log_alpha) * next_log_pis)
reward = next_time_steps.reward
if self._reward_noise_variance > 0:
reward_noise = tf.random.normal(
tf.shape(reward),
0.0,
self._reward_noise_variance,
seed=self._reward_seed_stream())
reward += reward_noise
td_targets = tf.stop_gradient(reward_scale_factor * reward +
gamma * next_time_steps.discount *
target_q_values)
pred_input = (time_steps.observation, actions)
pred_td_targets1, _ = self._critic_network_1(pred_input,
time_steps.step_type,
training=training)
pred_td_targets2, _ = self._critic_network_2(pred_input,
time_steps.step_type,
training=training)
critic_loss1 = td_errors_loss_fn(td_targets, pred_td_targets1)
critic_loss2 = td_errors_loss_fn(td_targets, pred_td_targets2)
critic_loss = critic_loss1 + critic_loss2
if critic_loss.shape.rank > 1:
# Sum over the time dimension.
critic_loss = tf.reduce_sum(critic_loss,
axis=range(1,
critic_loss.shape.rank))
agg_loss = common.aggregate_losses(
per_example_loss=critic_loss,
sample_weight=weights,
regularization_loss=(self._critic_network_1.losses +
self._critic_network_2.losses))
critic_loss = agg_loss.total_loss
self._critic_loss_debug_summaries(td_targets, pred_td_targets1,
pred_td_targets2)
return critic_loss
def __init__(self,
nested_layers: types.NestedLayer,
input_spec: typing.Optional[types.NestedTensorSpec] = None,
name: typing.Optional[typing.Text] = None):
"""Create a Sequential Network.
Args:
nested_layers: A nest of layers and/or networks. These will be used
to process the inputs (input nest structure will have to match this
structure). Any layers that are subclasses of
`tf.keras.layers.{RNN,LSTM,GRU,...}` are wrapped in
`tf_agents.keras_layers.RNNWrapper`.
input_spec: (Optional.) A nest of `tf.TypeSpec` representing the
input observations. The structure of `input_spec` must match
that of `nested_layers`.
name: (Optional.) Network name.
Raises:
TypeError: If any of the layers are not instances of keras `Layer`.
ValueError: If `input_spec` is provided but its nest structure does
not match that of `nested_layers`.
RuntimeError: If not `tf.executing_eagerly()`; as this is required to
be able to create deep copies of layers in `layers`.
"""
if not tf.executing_eagerly():
raise RuntimeError(
'Not executing eagerly - cannot make deep copies of `nested_layers`.'
)
flat_nested_layers = tf.nest.flatten(nested_layers)
for layer in flat_nested_layers:
if not isinstance(layer, tf.keras.layers.Layer):
raise TypeError(
'Expected all layers to be instances of keras Layer, but saw'
': \'{}\''.format(layer))
if input_spec is not None:
nest_utils.assert_same_structure(
nested_layers,
input_spec,
message=
('`nested_layers` and `input_spec` do not have matching structures'
))
flat_input_spec = tf.nest.flatten(input_spec)
else:
flat_input_spec = [None] * len(flat_nested_layers)
# Wrap in Sequential if necessary.
flat_nested_layers = [
sequential.Sequential([m], s)
if not isinstance(m, network.Network) else m
for (s, m) in zip(flat_input_spec, flat_nested_layers)
]
flat_nested_layers_state_specs = [
m.state_spec for m in flat_nested_layers
]
nested_layers = tf.nest.pack_sequence_as(nested_layers,
flat_nested_layers)
# We use flattened layers and states here instead of tf.nest.map_structure
# for several reason. One is that we perform several operations against
# the layers and we want to avoid calling into tf.nest.map* multiple times.
# But the main reason is that network states have a different *structure*
# than the layers; e.g., `nested_layers` may just be tf.keras.layers.LSTM,
# but the states would then have structure `[.,.]`. Passing these in
# as args to tf.nest.map_structure causes it to fail. Instead we would
# have to use nest.map_structure_up_to -- but that function is not part
# of the public TF API. However, if we do everything in flatland and then
# use pack_sequence_as, we bypass the more rigid structure tests.
state_spec = tf.nest.pack_sequence_as(nested_layers,
flat_nested_layers_state_specs)
super(NestMap, self).__init__(input_tensor_spec=input_spec,
state_spec=state_spec,
name=name)
self._nested_layers = nested_layers
def actor_loss(self,
time_steps,
actions,
next_time_steps,
weights=None):
"""Computes the actor_loss for SAC training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
Returns:
actor_loss: A scalar actor loss.
"""
prev_time_steps, prev_actions, time_steps = time_steps, actions, next_time_steps # pylint: disable=line-too-long
with tf.name_scope('actor_loss'):
nest_utils.assert_same_structure(time_steps, self.time_step_spec)
actions, log_pi = self._actions_and_log_probs(time_steps)
target_input = (time_steps.observation, actions)
target_q_values1, _ = self._critic_network_1(
target_input, step_type=time_steps.step_type, training=False)
target_q_values2, _ = self._critic_network_2(
target_input, step_type=time_steps.step_type, training=False)
target_q_values = tf.minimum(target_q_values1, target_q_values2)
actor_loss = tf.exp(self._log_alpha) * log_pi - target_q_values
### Flatten time dimension. We'll add it back when adding the loss.
num_outer_dims = nest_utils.get_outer_rank(time_steps,
self.time_step_spec)
has_time_dim = (num_outer_dims == 2)
if has_time_dim:
batch_squash = utils.BatchSquash(2) # Squash B, and T dims.
obs = batch_squash.flatten(time_steps.observation)
prev_obs = batch_squash.flatten(prev_time_steps.observation)
prev_actions = batch_squash.flatten(prev_actions)
else:
obs = time_steps.observation
prev_obs = prev_time_steps.observation
z = self._actor_network._z_encoder(obs, training=True) # pylint: disable=protected-access
prior = self._actor_network._predictor((prev_obs, prev_actions), # pylint: disable=protected-access
training=True)
# kl is a vector of length batch_size, which has already been summed over
# the latent dimension z.
kl = tfp.distributions.kl_divergence(z, prior)
if has_time_dim:
kl = batch_squash.unflatten(kl)
kl_coef = tf.stop_gradient(
tf.exp(self._actor_network._log_kl_coefficient)) # pylint: disable=protected-access
# The actor loss trains both the predictor and the encoder.
actor_loss += kl_coef * kl
if actor_loss.shape.rank > 1:
# Sum over the time dimension.
actor_loss = tf.reduce_sum(
actor_loss, axis=range(1, actor_loss.shape.rank))
reg_loss = self._actor_network.losses if self._actor_network else None
agg_loss = common.aggregate_losses(
per_example_loss=actor_loss,
sample_weight=weights,
regularization_loss=reg_loss)
actor_loss = agg_loss.total_loss
self._actor_loss_debug_summaries(actor_loss, actions, log_pi,
target_q_values, time_steps)
tf.compat.v2.summary.scalar(
name='encoder_kl',
data=tf.reduce_mean(kl),
step=self.train_step_counter)
return actor_loss
def __call__(self, inputs, *args, **kwargs):
"""A wrapper around `Network.call`.
A typical `call` method in a class subclassing `Network` will have a
signature that accepts `inputs`, as well as other `*args` and `**kwargs`.
`call` can optionally also accept `step_type` and `network_state`
(if `state_spec != ()` is not trivial). e.g.:
```python
def call(self,
inputs,
step_type=None,
network_state=(),
training=False):
...
return outputs, new_network_state
```
We will validate the first argument (`inputs`)
against `self.input_tensor_spec` if one is available.
If a `network_state` kwarg is given it is also validated against
`self.state_spec`. Similarly, the return value of the `call` method is
expected to be a tuple/list with 2 values: `(output, new_state)`.
We validate `new_state` against `self.state_spec`.
If no `network_state` kwarg is given (or if empty `network_state = ()` is
given, it is up to `call` to assume a proper "empty" state, and to
emit an appropriate `output_state`.
Args:
inputs: The input to `self.call`, matching `self.input_tensor_spec`.
*args: Additional arguments to `self.call`.
**kwargs: Additional keyword arguments to `self.call`.
These can include `network_state` and `step_type`. `step_type` is
required if the network's `call` requires it. `network_state` is
required if the underlying network's `call` requires it.
Returns:
A tuple `(outputs, new_network_state)`.
"""
if self.input_tensor_spec is not None:
nest_utils.assert_same_structure(
inputs,
self.input_tensor_spec,
message="inputs and input_tensor_spec structures do not match")
call_argspec = tf_inspect.getargspec(self.call)
# Convert *args, **kwargs to a canonical kwarg representation.
normalized_kwargs = tf_inspect.getcallargs(self.call, inputs, *args,
**kwargs)
# TODO(b/156315434): Rename network_state to just state.
network_state = normalized_kwargs.get("network_state", None)
normalized_kwargs.pop("self", None)
if network_state not in (None, ()):
nest_utils.assert_same_structure(
network_state,
self.state_spec,
message="network_state and state_spec structures do not match")
if "step_type" not in call_argspec.args and not call_argspec.keywords:
normalized_kwargs.pop("step_type", None)
if (network_state in (None, ())
and "network_state" not in call_argspec.args
and not call_argspec.keywords):
normalized_kwargs.pop("network_state", None)
outputs, new_state = super(Network, self).__call__(**normalized_kwargs)
nest_utils.assert_same_structure(
new_state,
self.state_spec,
message=
"network output state and state_spec structures do not match")
return outputs, new_state
