def call(self, trajectory: Trajectory) -> Trajectory:
time_step = TimeStep(trajectory.step_type, trajectory.reward, trajectory.discount,
trajectory.observation)
action_dist = self._policy.distribution(time_step).action
# If the action distribution is in fact a tuple of distributions (one for each resource set)
# then we need to index into them to attain the underlying distribution which can then be
# used to attain probabilities. This is only the case where there are multiple resource
# sets.
for i in self._action_indices[:-1]:
action_dist = action_dist[i]
action_probs = action_dist.probs_parameter()
# Zero out batch indices where a new episode is starting.
self._probability_accumulator.assign(
tf.where(trajectory.is_first(), tf.zeros_like(self._probability_accumulator),
self._probability_accumulator))
self._count_accumulator.assign(
tf.where(trajectory.is_first(), tf.zeros_like(self._count_accumulator),
self._count_accumulator))
# Update accumulators with probability and count increments.
self._probability_accumulator.assign_add(action_probs[..., 0, self._action_indices[-1]])
self._count_accumulator.assign_add(tf.ones_like(self._count_accumulator))
# Add final cumulants to buffer at the end of episodes.
last_episode_indices = tf.squeeze(tf.where(trajectory.is_last()), axis=-1)
for idx in last_episode_indices:
self._buffer.add(self._probability_accumulator[idx] / self._count_accumulator[idx])
return trajectory
def call(self, trajectory: traj.Trajectory):
if trajectory.step_type.ndim == 0:
trajectory = nest_utils.batch_nested_array(trajectory)
completed_episodes = np.sum(trajectory.is_last().astype(np.int64))
self._np_state.number_episodes += completed_episodes
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)