def _action(self, time_step, policy_state, seed):
observation = time_step.observation
mask = None
observation_and_action_constraint_splitter = (
self.observation_and_action_constraint_splitter)
if observation_and_action_constraint_splitter is not None:
observation, mask = observation_and_action_constraint_splitter(
observation)
self._check_observation_shape(observation)
observation = tf.nest.map_structure(
lambda t: tf.cast(t, dtype=self._dtype), observation)
# Pass the observations through the encoding network.
encoded_observation, _ = self._encoding_network(observation)
chosen_actions, est_mean_rewards = tf.cond(
self._actions_from_reward_layer,
lambda: self._get_actions_from_reward_layer(
encoded_observation, mask),
lambda: self._get_actions_from_linucb(encoded_observation, mask))
arm_observations = ()
if self._accepts_per_arm_features:
arm_observations = observation[
bandit_spec_utils.PER_ARM_FEATURE_KEY]
policy_info = policy_utilities.populate_policy_info(
arm_observations, chosen_actions, (), est_mean_rewards,
self._emit_policy_info, self._accepts_per_arm_features)
return policy_step.PolicyStep(chosen_actions, policy_state,
policy_info)
def _action(self, time_step, policy_state, seed):
observation = time_step.observation
mask = None
observation_and_action_constraint_splitter = (
self.observation_and_action_constraint_splitter)
if observation_and_action_constraint_splitter is not None:
observation, mask = observation_and_action_constraint_splitter(
observation)
# Pass the observations through the encoding network.
encoded_observation, _ = self._encoding_network(observation)
encoded_observation = tf.cast(encoded_observation, dtype=self._dtype)
if tf.distribute.has_strategy():
if self._distributed_use_reward_layer:
chosen_actions, est_mean_rewards, est_rewards_optimistic = (
self._get_actions_from_reward_layer(encoded_observation, mask))
else:
chosen_actions, est_mean_rewards, est_rewards_optimistic = (
self._get_actions_from_linucb(encoded_observation, mask))
else:
chosen_actions, est_mean_rewards, est_rewards_optimistic = tf.cond(
self._actions_from_reward_layer,
# pylint: disable=g-long-lambda
lambda: self._get_actions_from_reward_layer(
encoded_observation, mask),
lambda: self._get_actions_from_linucb(encoded_observation, mask))
arm_observations = ()
if self._accepts_per_arm_features:
arm_observations = observation[bandit_spec_utils.PER_ARM_FEATURE_KEY]
policy_info = policy_utilities.populate_policy_info(
arm_observations, chosen_actions, est_rewards_optimistic,
est_mean_rewards, self._emit_policy_info,
self._accepts_per_arm_features)
return policy_step.PolicyStep(chosen_actions, policy_state, policy_info)
def _distribution(self, time_step, policy_state):
observation = time_step.observation
if self.observation_and_action_constraint_splitter is not None:
observation, _ = self.observation_and_action_constraint_splitter(
observation)
observation = tf.nest.map_structure(
lambda o: tf.cast(o, dtype=self._dtype), observation)
global_observation, arm_observations = self._split_observation(
observation)
if self._add_bias:
# The bias is added via a constant 1 feature.
global_observation = tf.concat([
global_observation,
tf.ones([tf.shape(global_observation)[0], 1],
dtype=self._dtype)
],
axis=1)
# Check the shape of the observation matrix. The observations can be
# batched.
if not global_observation.shape.is_compatible_with(
[None, self._global_context_dim]):
raise ValueError(
'Global observation shape is expected to be {}. Got {}.'.
format([None, self._global_context_dim],
global_observation.shape.as_list()))
global_observation = tf.reshape(global_observation,
[-1, self._global_context_dim])
est_rewards = []
confidence_intervals = []
for k in range(self._num_actions):
current_observation = self._get_current_observation(
global_observation, arm_observations, k)
model_index = policy_utilities.get_model_index(
k, self._accepts_per_arm_features)
if self._use_eigendecomp:
q_t_b = tf.matmul(
self._eig_matrix[model_index],
tf.linalg.matrix_transpose(current_observation),
transpose_a=True)
lambda_inv = tf.divide(
tf.ones_like(self._eig_vals[model_index]),
self._eig_vals[model_index] + self._tikhonov_weight)
a_inv_x = tf.matmul(self._eig_matrix[model_index],
tf.einsum('j,jk->jk', lambda_inv, q_t_b))
else:
a_inv_x = linalg.conjugate_gradient_solve(
self._cov_matrix[model_index] + self._tikhonov_weight *
tf.eye(self._overall_context_dim, dtype=self._dtype),
tf.linalg.matrix_transpose(current_observation))
est_mean_reward = tf.einsum('j,jk->k',
self._data_vector[model_index],
a_inv_x)
est_rewards.append(est_mean_reward)
ci = tf.reshape(
tf.linalg.tensor_diag_part(
tf.matmul(current_observation, a_inv_x)), [-1, 1])
confidence_intervals.append(ci)
if self._exploration_strategy == ExplorationStrategy.optimistic:
optimistic_estimates = [
tf.reshape(mean_reward, [-1, 1]) +
self._alpha * tf.sqrt(confidence)
for mean_reward, confidence in zip(est_rewards,
confidence_intervals)
]
# Keeping the batch dimension during the squeeze, even if batch_size == 1.
rewards_for_argmax = tf.squeeze(tf.stack(optimistic_estimates,
axis=-1),
axis=[1])
elif self._exploration_strategy == ExplorationStrategy.sampling:
mu_sampler = tfd.Normal(
loc=tf.stack(est_rewards, axis=-1),
scale=self._alpha * tf.sqrt(
tf.squeeze(tf.stack(confidence_intervals, axis=-1),
axis=1)))
rewards_for_argmax = mu_sampler.sample()
else:
raise ValueError('Exploraton strategy %s not implemented.' %
self._exploration_strategy)
mask = constraints.construct_mask_from_multiple_sources(
time_step.observation,
self._observation_and_action_constraint_splitter, (),
self._num_actions)
if mask is not None:
chosen_actions = policy_utilities.masked_argmax(
rewards_for_argmax,
mask,
output_type=tf.nest.flatten(self._action_spec)[0].dtype)
else:
chosen_actions = tf.argmax(rewards_for_argmax,
axis=-1,
output_type=tf.nest.flatten(
self._action_spec)[0].dtype)
action_distributions = tfp.distributions.Deterministic(
loc=chosen_actions)
policy_info = policy_utilities.populate_policy_info(
arm_observations, chosen_actions, rewards_for_argmax,
tf.stack(est_rewards, axis=-1), self._emit_policy_info,
self._accepts_per_arm_features)
return policy_step.PolicyStep(action_distributions, policy_state,
policy_info)
