def _populate_policy_info_spec(self, observation_spec,
observation_and_action_constraint_splitter):
predicted_rewards_mean = ()
if (policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN
in self._emit_policy_info):
predicted_rewards_mean = tensor_spec.TensorSpec(
[self._num_actions], dtype=self._dtype)
predicted_rewards_optimistic = ()
if (policy_utilities.InfoFields.PREDICTED_REWARDS_OPTIMISTIC
in self._emit_policy_info):
predicted_rewards_optimistic = tensor_spec.TensorSpec(
[self._num_actions], dtype=self._dtype)
predicted_rewards_sampled = ()
if (policy_utilities.InfoFields.PREDICTED_REWARDS_SAMPLED
in self._emit_policy_info):
predicted_rewards_sampled = tensor_spec.TensorSpec(
[self._num_actions], dtype=self._dtype)
if self._accepts_per_arm_features:
# The features for the chosen arm is saved to policy_info.
chosen_arm_features_info = (
policy_utilities.create_chosen_arm_features_info_spec(
observation_spec))
info_spec = policy_utilities.PerArmPolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
predicted_rewards_optimistic=predicted_rewards_optimistic,
predicted_rewards_sampled=predicted_rewards_sampled,
chosen_arm_features=chosen_arm_features_info)
else:
info_spec = policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
predicted_rewards_optimistic=predicted_rewards_optimistic,
predicted_rewards_sampled=predicted_rewards_sampled)
return info_spec
def _get_action_step(action, num_agents, num_actions):
batch_size = tf.shape(action)[0]
choices = tf.random.uniform(
shape=tf.shape(action), minval=0, maxval=num_agents - 1, dtype=tf.int32)
return policy_step.PolicyStep(
action=tf.convert_to_tensor(action),
info={
mixture_policy.MIXTURE_AGENT_ID:
choices,
mixture_policy.SUBPOLICY_INFO:
policy_utilities.PolicyInfo(
predicted_rewards_mean=tf.zeros([batch_size, num_actions]))
})
def _get_action_step(action, num_agents, num_actions, emit_policy_info):
batch_size = tf.shape(action)[0]
choices = tf.constant(num_agents - 1, shape=action.shape, dtype=tf.int32)
predicted_rewards_mean = (
tf.zeros([batch_size, num_actions]) if emit_policy_info else ())
return policy_step.PolicyStep(
action=tf.convert_to_tensor(action),
info={
mixture_policy.MIXTURE_AGENT_ID:
choices,
mixture_policy.SUBPOLICY_INFO:
policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean)
})
def __init__(self,
encoding_network: types.Network,
encoding_dim: int,
reward_layer: tf.keras.layers.Dense,
epsilon_greedy: float,
actions_from_reward_layer: types.Bool,
cov_matrix: Sequence[types.Float],
data_vector: Sequence[types.Float],
num_samples: Sequence[types.Int],
time_step_spec: types.TimeStep,
alpha: float = 1.0,
emit_policy_info: Sequence[Text] = (),
emit_log_probability: bool = False,
accepts_per_arm_features: bool = False,
distributed_use_reward_layer: bool = False,
observation_and_action_constraint_splitter: Optional[
types.Splitter] = None,
name: Optional[Text] = None):
"""Initializes `NeuralLinUCBPolicy`.
Args:
encoding_network: network that encodes the observations.
encoding_dim: (int) dimension of the encoded observations.
reward_layer: final layer that predicts the expected reward per arm. In
case the policy accepts per-arm features, the output of this layer has
to be a scalar. This is because in the per-arm case, all encoded
observations have to go through the same computation to get the reward
estimates. The `num_actions` dimension of the encoded observation is
treated as a batch dimension in the reward layer.
epsilon_greedy: (float) representing the probability of choosing a random
action instead of the greedy action.
actions_from_reward_layer: (boolean variable) whether to get actions from
the reward layer or from LinUCB.
cov_matrix: list of the covariance matrices. There exists one covariance
matrix per arm, unless the policy accepts per-arm features, in which
case this list must have a single element.
data_vector: list of the data vectors. A data vector is a weighted sum
of the observations, where the weight is the corresponding reward. Each
arm has its own data vector, unless the policy accepts per-arm features,
in which case this list must have a single element.
num_samples: list of number of samples per arm. If the policy accepts per-
arm features, this is a single-element list counting the number of
steps.
time_step_spec: A `TimeStep` spec of the expected time_steps.
alpha: (float) non-negative weight multiplying the confidence intervals.
emit_policy_info: (tuple of strings) what side information we want to get
as part of the policy info. Allowed values can be found in
`policy_utilities.PolicyInfo`.
emit_log_probability: (bool) whether to emit log probabilities.
accepts_per_arm_features: (bool) Whether the policy accepts per-arm
features.
distributed_use_reward_layer: (bool) Whether to pick the actions using
the network or use LinUCB. This applies only in distributed training
setting and has a similar role to the `actions_from_reward_layer`
mentioned above.
observation_and_action_constraint_splitter: A function used for masking
valid/invalid actions with each state of the environment. The function
takes in a full observation and returns a tuple consisting of 1) the
part of the observation intended as input to the bandit policy and 2)
the mask. The mask should be a 0-1 `Tensor` of shape
`[batch_size, num_actions]`. This function should also work with a
`TensorSpec` as input, and should output `TensorSpec` objects for the
observation and mask.
name: The name of this policy.
"""
policy_utilities.check_no_mask_with_arm_features(
accepts_per_arm_features,
observation_and_action_constraint_splitter)
encoding_network.create_variables()
self._encoding_network = encoding_network
self._reward_layer = reward_layer
self._encoding_dim = encoding_dim
if accepts_per_arm_features and reward_layer.units != 1:
raise ValueError(
'The output dimension of the reward layer must be 1, got'
' {}'.format(reward_layer.units))
if not isinstance(cov_matrix, (list, tuple)):
raise ValueError(
'cov_matrix must be a list of matrices (Tensors).')
self._cov_matrix = cov_matrix
if not isinstance(data_vector, (list, tuple)):
raise ValueError(
'data_vector must be a list of vectors (Tensors).')
self._data_vector = data_vector
if not isinstance(num_samples, (list, tuple)):
raise ValueError(
'num_samples must be a list of vectors (Tensors).')
self._num_samples = num_samples
self._alpha = alpha
self._actions_from_reward_layer = actions_from_reward_layer
self._epsilon_greedy = epsilon_greedy
self._dtype = self._data_vector[0].dtype
self._distributed_use_reward_layer = distributed_use_reward_layer
if len(cov_matrix) != len(data_vector):
raise ValueError(
'The size of list cov_matrix must match the size of '
'list data_vector. Got {} for cov_matrix and {} '
'for data_vector'.format(len(self._cov_matrix),
len((data_vector))))
if len(num_samples) != len(cov_matrix):
raise ValueError('The size of num_samples must match the size of '
'list cov_matrix. Got {} for num_samples and {} '
'for cov_matrix'.format(len(self._num_samples),
len((cov_matrix))))
self._accepts_per_arm_features = accepts_per_arm_features
if observation_and_action_constraint_splitter is not None:
context_spec, _ = observation_and_action_constraint_splitter(
time_step_spec.observation)
else:
context_spec = time_step_spec.observation
if accepts_per_arm_features:
self._num_actions = tf.nest.flatten(context_spec[
bandit_spec_utils.PER_ARM_FEATURE_KEY])[0].shape.as_list()[0]
self._num_models = 1
else:
self._num_actions = len(cov_matrix)
self._num_models = self._num_actions
cov_matrix_dim = tf.compat.dimension_value(cov_matrix[0].shape[0])
if self._encoding_dim != cov_matrix_dim:
raise ValueError('The dimension of matrix `cov_matrix` must match '
'encoding dimension {}.'
'Got {} for `cov_matrix`.'.format(
self._encoding_dim, cov_matrix_dim))
data_vector_dim = tf.compat.dimension_value(data_vector[0].shape[0])
if self._encoding_dim != data_vector_dim:
raise ValueError(
'The dimension of vector `data_vector` must match '
'encoding dimension {}. '
'Got {} for `data_vector`.'.format(self._encoding_dim,
data_vector_dim))
action_spec = tensor_spec.BoundedTensorSpec(shape=(),
dtype=tf.int32,
minimum=0,
maximum=self._num_actions -
1,
name='action')
self._emit_policy_info = emit_policy_info
predicted_rewards_mean = ()
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info:
predicted_rewards_mean = tensor_spec.TensorSpec(
[self._num_actions], dtype=tf.float32)
predicted_rewards_optimistic = ()
if (policy_utilities.InfoFields.PREDICTED_REWARDS_OPTIMISTIC
in emit_policy_info):
predicted_rewards_optimistic = tensor_spec.TensorSpec(
[self._num_actions], dtype=tf.float32)
if accepts_per_arm_features:
chosen_arm_features_info_spec = (
policy_utilities.create_chosen_arm_features_info_spec(
time_step_spec.observation))
info_spec = policy_utilities.PerArmPolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
predicted_rewards_optimistic=predicted_rewards_optimistic,
chosen_arm_features=chosen_arm_features_info_spec)
else:
info_spec = policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
predicted_rewards_optimistic=predicted_rewards_optimistic)
super(NeuralLinUCBPolicy,
self).__init__(time_step_spec=time_step_spec,
action_spec=action_spec,
emit_log_probability=emit_log_probability,
observation_and_action_constraint_splitter=(
observation_and_action_constraint_splitter),
info_spec=info_spec,
name=name)
def __init__(self,
time_step_spec: types.TimeStep,
action_spec: types.NestedTensorSpec,
reward_network: types.Network,
temperature: types.FloatOrReturningFloat = 1.0,
observation_and_action_constraint_splitter: Optional[
types.Splitter] = None,
accepts_per_arm_features: bool = False,
constraints: Tuple[constr.NeuralConstraint, ...] = (),
emit_policy_info: Tuple[Text, ...] = (),
name: Optional[Text] = None):
"""Builds a BoltzmannRewardPredictionPolicy given a reward network.
This policy takes a tf_agents.Network predicting rewards and chooses an
action with weighted probabilities (i.e., using a softmax over the network
estimates of value for each action).
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
reward_network: An instance of a `tf_agents.network.Network`,
callable via `network(observation, step_type) -> (output, final_state)`.
temperature: float or callable that returns a float. The temperature used
in the Boltzmann exploration.
observation_and_action_constraint_splitter: A function used for masking
valid/invalid actions with each state of the environment. The function
takes in a full observation and returns a tuple consisting of 1) the
part of the observation intended as input to the network and 2) the
mask. The mask should be a 0-1 `Tensor` of shape
`[batch_size, num_actions]`. This function should also work with a
`TensorSpec` as input, and should output `TensorSpec` objects for the
observation and mask.
accepts_per_arm_features: (bool) Whether the policy accepts per-arm
features.
constraints: iterable of constraints objects that are instances of
`tf_agents.bandits.agents.NeuralConstraint`.
emit_policy_info: (tuple of strings) what side information we want to get
as part of the policy info. Allowed values can be found in
`policy_utilities.PolicyInfo`.
name: The name of this policy. All variables in this module will fall
under that name. Defaults to the class name.
Raises:
NotImplementedError: If `action_spec` contains more than one
`BoundedTensorSpec` or the `BoundedTensorSpec` is not valid.
"""
policy_utilities.check_no_mask_with_arm_features(
accepts_per_arm_features,
observation_and_action_constraint_splitter)
flat_action_spec = tf.nest.flatten(action_spec)
if len(flat_action_spec) > 1:
raise NotImplementedError(
'action_spec can only contain a single BoundedTensorSpec.')
self._temperature = temperature
action_spec = flat_action_spec[0]
if (not tensor_spec.is_bounded(action_spec)
or not tensor_spec.is_discrete(action_spec)
or action_spec.shape.rank > 1
or action_spec.shape.num_elements() != 1):
raise NotImplementedError(
'action_spec must be a BoundedTensorSpec of type int32 and shape (). '
'Found {}.'.format(action_spec))
self._expected_num_actions = action_spec.maximum - action_spec.minimum + 1
self._action_offset = action_spec.minimum
reward_network.create_variables()
self._reward_network = reward_network
self._constraints = constraints
self._emit_policy_info = emit_policy_info
predicted_rewards_mean = ()
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info:
predicted_rewards_mean = tensor_spec.TensorSpec(
[self._expected_num_actions])
bandit_policy_type = ()
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE in emit_policy_info:
bandit_policy_type = (
policy_utilities.create_bandit_policy_type_tensor_spec(
shape=[1]))
if accepts_per_arm_features:
# The features for the chosen arm is saved to policy_info.
chosen_arm_features_info = (
policy_utilities.create_chosen_arm_features_info_spec(
time_step_spec.observation))
info_spec = policy_utilities.PerArmPolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
bandit_policy_type=bandit_policy_type,
chosen_arm_features=chosen_arm_features_info)
else:
info_spec = policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
bandit_policy_type=bandit_policy_type)
self._accepts_per_arm_features = accepts_per_arm_features
super(BoltzmannRewardPredictionPolicy,
self).__init__(time_step_spec,
action_spec,
policy_state_spec=reward_network.state_spec,
clip=False,
info_spec=info_spec,
emit_log_probability='log_probability'
in emit_policy_info,
observation_and_action_constraint_splitter=(
observation_and_action_constraint_splitter),
name=name)
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)
predictions, policy_state = self._reward_network(
observation, time_step.step_type, policy_state)
batch_size = tf.shape(predictions)[0]
if isinstance(self._reward_network,
heteroscedastic_q_network.HeteroscedasticQNetwork):
predicted_reward_values = predictions.q_value_logits
else:
predicted_reward_values = predictions
predicted_reward_values.shape.with_rank_at_least(2)
predicted_reward_values.shape.with_rank_at_most(3)
if predicted_reward_values.shape[
-1] is not None and predicted_reward_values.shape[
-1] != self._expected_num_actions:
raise ValueError(
'The number of actions ({}) does not match the reward_network output'
' size ({}).'.format(self._expected_num_actions,
predicted_reward_values.shape[1]))
mask = constr.construct_mask_from_multiple_sources(
time_step.observation,
self._observation_and_action_constraint_splitter,
self._constraints, self._expected_num_actions)
# Apply the temperature scaling, needed for Boltzmann exploration.
logits = predicted_reward_values / self._get_temperature_value()
# Apply masking if needed. Overwrite the logits for invalid actions to
# logits.dtype.min.
if mask is not None:
almost_neg_inf = tf.constant(logits.dtype.min, dtype=logits.dtype)
logits = tf.compat.v2.where(tf.cast(mask, tf.bool), logits,
almost_neg_inf)
if self._action_offset != 0:
distribution = shifted_categorical.ShiftedCategorical(
logits=logits,
dtype=self._action_spec.dtype,
shift=self._action_offset)
else:
distribution = tfp.distributions.Categorical(
logits=logits, dtype=self._action_spec.dtype)
actions = distribution.sample()
bandit_policy_values = tf.fill(
[batch_size, 1], policy_utilities.BanditPolicyType.BOLTZMANN)
if self._accepts_per_arm_features:
# Saving the features for the chosen action to the policy_info.
def gather_observation(obs):
return tf.gather(params=obs, indices=actions, batch_dims=1)
chosen_arm_features = tf.nest.map_structure(
gather_observation,
observation[bandit_spec_utils.PER_ARM_FEATURE_KEY])
policy_info = policy_utilities.PerArmPolicyInfo(
log_probability=distribution.log_prob(actions)
if policy_utilities.InfoFields.LOG_PROBABILITY
in self._emit_policy_info else (),
predicted_rewards_mean=(
predicted_reward_values
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN
in self._emit_policy_info else ()),
bandit_policy_type=(
bandit_policy_values
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE
in self._emit_policy_info else ()),
chosen_arm_features=chosen_arm_features)
else:
policy_info = policy_utilities.PolicyInfo(
log_probability=distribution.log_prob(actions)
if policy_utilities.InfoFields.LOG_PROBABILITY
in self._emit_policy_info else (),
predicted_rewards_mean=(
predicted_reward_values
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN
in self._emit_policy_info else ()),
bandit_policy_type=(
bandit_policy_values
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE
in self._emit_policy_info else ()))
return policy_step.PolicyStep(
tfp.distributions.Deterministic(loc=actions), policy_state,
policy_info)
def _get_action_step(action):
return policy_step.PolicyStep(action=tf.convert_to_tensor(action),
info=policy_utilities.PolicyInfo())
def __init__(self,
time_step_spec: types.TimeStep,
action_spec: types.NestedTensorSpec,
alpha: Sequence[tf.Variable],
beta: Sequence[tf.Variable],
observation_and_action_constraint_splitter: Optional[
types.Splitter] = None,
emit_policy_info: Sequence[Text] = (),
name: Optional[Text] = None):
"""Builds a BernoulliThompsonSamplingPolicy.
For a reference, see e.g., Chapter 3 in "A Tutorial on Thompson Sampling" by
Russo et al. (https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf).
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
alpha: list or tuple of tf.Variable's. It holds the `alpha` parameter of
the beta distribution of each arm.
beta: list or tuple of tf.Variable's. It holds the `beta` parameter of the
beta distribution of each arm.
observation_and_action_constraint_splitter: A function used for masking
valid/invalid actions with each state of the environment. The function
takes in a full observation and returns a tuple consisting of 1) the
part of the observation intended as input to the network and 2) the
mask. The mask should be a 0-1 `Tensor` of shape
`[batch_size, num_actions]`. This function should also work with a
`TensorSpec` as input, and should output `TensorSpec` objects for the
observation and mask.
emit_policy_info: (tuple of strings) what side information we want to get
as part of the policy info. Allowed values can be found in
`policy_utilities.PolicyInfo`.
name: The name of this policy. All variables in this module will fall
under that name. Defaults to the class name.
Raises:
NotImplementedError: If `action_spec` contains more than one
`BoundedTensorSpec` or the `BoundedTensorSpec` is not valid.
"""
flat_action_spec = tf.nest.flatten(action_spec)
if len(flat_action_spec) > 1:
raise NotImplementedError(
'action_spec can only contain a single BoundedTensorSpec.')
action_spec = flat_action_spec[0]
if (not tensor_spec.is_bounded(action_spec)
or not tensor_spec.is_discrete(action_spec)
or action_spec.shape.rank > 1
or action_spec.shape.num_elements() != 1):
raise NotImplementedError(
'action_spec must be a BoundedTensorSpec of integer type and '
'shape (). Found {}.'.format(action_spec))
self._expected_num_actions = action_spec.maximum - action_spec.minimum + 1
if len(alpha) != self._expected_num_actions:
raise ValueError(
'The size of alpha parameters is expected to be equal '
'to the number of actions, but found to be {}'.format(
len(alpha)))
self._alpha = alpha
if len(alpha) != len(beta):
raise ValueError(
'The size of alpha parameters is expected to be equal '
'to the size of beta parameters')
self._beta = beta
self._emit_policy_info = emit_policy_info
predicted_rewards_mean = ()
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info:
predicted_rewards_mean = tensor_spec.TensorSpec(
[self._expected_num_actions])
predicted_rewards_sampled = ()
if policy_utilities.InfoFields.PREDICTED_REWARDS_SAMPLED in (
emit_policy_info):
predicted_rewards_sampled = tensor_spec.TensorSpec(
[self._expected_num_actions])
info_spec = policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
predicted_rewards_sampled=predicted_rewards_sampled)
super(BernoulliThompsonSamplingPolicy,
self).__init__(time_step_spec,
action_spec,
info_spec=info_spec,
emit_log_probability='log_probability'
in emit_policy_info,
observation_and_action_constraint_splitter=(
observation_and_action_constraint_splitter),
name=name)
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)
predictions, policy_state = self._reward_network(
observation, time_step.step_type, policy_state)
batch_size = tf.shape(predictions)[0]
if isinstance(self._reward_network,
heteroscedastic_q_network.HeteroscedasticQNetwork):
predicted_reward_values = predictions.q_value_logits
else:
predicted_reward_values = predictions
predicted_reward_values.shape.with_rank_at_least(2)
predicted_reward_values.shape.with_rank_at_most(3)
if predicted_reward_values.shape[
-1] is not None and predicted_reward_values.shape[
-1] != self._expected_num_actions:
raise ValueError(
'The number of actions ({}) does not match the reward_network output'
' size ({}).'.format(self._expected_num_actions,
predicted_reward_values.shape[1]))
mask = constr.construct_mask_from_multiple_sources(
time_step.observation, self._observation_and_action_constraint_splitter,
self._constraints, self._expected_num_actions)
# Argmax.
if mask is not None:
actions = policy_utilities.masked_argmax(
predicted_reward_values, mask, output_type=self.action_spec.dtype)
else:
actions = tf.argmax(
predicted_reward_values, axis=-1, output_type=self.action_spec.dtype)
actions += self._action_offset
bandit_policy_values = tf.fill([batch_size, 1],
policy_utilities.BanditPolicyType.GREEDY)
if self._accepts_per_arm_features:
# Saving the features for the chosen action to the policy_info.
def gather_observation(obs):
return tf.gather(params=obs, indices=actions, batch_dims=1)
chosen_arm_features = tf.nest.map_structure(
gather_observation,
observation[bandit_spec_utils.PER_ARM_FEATURE_KEY])
policy_info = policy_utilities.PerArmPolicyInfo(
log_probability=tf.zeros([batch_size], tf.float32) if
policy_utilities.InfoFields.LOG_PROBABILITY in self._emit_policy_info
else (),
predicted_rewards_mean=(
predicted_reward_values if policy_utilities.InfoFields
.PREDICTED_REWARDS_MEAN in self._emit_policy_info else ()),
bandit_policy_type=(bandit_policy_values
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE
in self._emit_policy_info else ()),
chosen_arm_features=chosen_arm_features)
else:
policy_info = policy_utilities.PolicyInfo(
log_probability=tf.zeros([batch_size], tf.float32) if
policy_utilities.InfoFields.LOG_PROBABILITY in self._emit_policy_info
else (),
predicted_rewards_mean=(
predicted_reward_values if policy_utilities.InfoFields
.PREDICTED_REWARDS_MEAN in self._emit_policy_info else ()),
bandit_policy_type=(bandit_policy_values
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE
in self._emit_policy_info else ()))
return policy_step.PolicyStep(
tfp.distributions.Deterministic(loc=actions), policy_state, policy_info)
def __init__(
self,
time_step_spec: Optional[ts.TimeStep],
action_spec: Optional[types.NestedBoundedTensorSpec],
scalarizer: multi_objective_scalarizer.Scalarizer,
objective_networks: Sequence[Network],
observation_and_action_constraint_splitter: types.Splitter = None,
accepts_per_arm_features: bool = False,
emit_policy_info: Tuple[Text, ...] = (),
name: Optional[Text] = None):
"""Builds a GreedyMultiObjectiveNeuralPolicy based on multiple networks.
This policy takes an iterable of `tf_agents.Network`, each responsible for
predicting a specific objective, along with a `Scalarizer` object to
generate an action by maximizing the scalarized objective, i.e., the output
of the `Scalarizer` applied to the multiple predicted objectives by the
networks.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
scalarizer: A
`tf_agents.bandits.multi_objective.multi_objective_scalarizer.Scalarizer`
object that implements scalarization of multiple objectives into a
single scalar reward.
objective_networks: A Sequence of `tf_agents.network.Network` objects to
be used by the policy. Each network will be called with
call(observation, step_type) and is expected to provide a prediction for
a specific objective for all actions.
observation_and_action_constraint_splitter: A function used for masking
valid/invalid actions with each state of the environment. The function
takes in a full observation and returns a tuple consisting of 1) the
part of the observation intended as input to the network and 2) the
mask. The mask should be a 0-1 `Tensor` of shape `[batch_size,
num_actions]`. This function should also work with a `TensorSpec` as
input, and should output `TensorSpec` objects for the observation and
mask.
accepts_per_arm_features: (bool) Whether the policy accepts per-arm
features.
emit_policy_info: (tuple of strings) what side information we want to get
as part of the policy info. Allowed values can be found in
`policy_utilities.PolicyInfo`.
name: The name of this policy. All variables in this module will fall
under that name. Defaults to the class name.
Raises:
NotImplementedError: If `action_spec` contains more than one
`BoundedTensorSpec` or the `BoundedTensorSpec` is not valid.
NotImplementedError: If `action_spec` is not a `BoundedTensorSpec` of type
int32 and shape ().
ValueError: If `objective_networks` has fewer than two networks.
ValueError: If `accepts_per_arm_features` is true but `time_step_spec` is
None.
"""
policy_utilities.check_no_mask_with_arm_features(
accepts_per_arm_features,
observation_and_action_constraint_splitter)
flat_action_spec = tf.nest.flatten(action_spec)
if len(flat_action_spec) > 1:
raise NotImplementedError(
'action_spec can only contain a single BoundedTensorSpec.')
action_spec = flat_action_spec[0]
if (not tensor_spec.is_bounded(action_spec)
or not tensor_spec.is_discrete(action_spec)
or action_spec.shape.rank > 1
or action_spec.shape.num_elements() != 1):
raise NotImplementedError(
'action_spec must be a BoundedTensorSpec of type int32 and shape (). '
'Found {}.'.format(action_spec))
self._expected_num_actions = action_spec.maximum - action_spec.minimum + 1
self._action_offset = action_spec.minimum
policy_state_spec = []
for network in objective_networks:
policy_state_spec.append(network.state_spec)
network.create_variables()
self._objective_networks = objective_networks
self._scalarizer = scalarizer
self._num_objectives = len(self._objective_networks)
if self._num_objectives < 2:
raise ValueError(
'Number of objectives should be at least two, but found to be {}'
.format(self._num_objectives))
self._emit_policy_info = emit_policy_info
predicted_rewards_mean = ()
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info:
predicted_rewards_mean = tensor_spec.TensorSpec(
[self._num_objectives, self._expected_num_actions])
scalarized_predicted_rewards_mean = ()
if (policy_utilities.InfoFields.
MULTIOBJECTIVE_SCALARIZED_PREDICTED_REWARDS_MEAN
in emit_policy_info):
scalarized_predicted_rewards_mean = tensor_spec.TensorSpec(
[self._expected_num_actions])
bandit_policy_type = ()
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE in emit_policy_info:
bandit_policy_type = (
policy_utilities.create_bandit_policy_type_tensor_spec(
shape=[1]))
if accepts_per_arm_features:
if time_step_spec is None:
raise ValueError(
'time_step_spec should not be None for per-arm-features policies, '
'but found to be.')
# The features for the chosen arm is saved to policy_info.
chosen_arm_features_info = (
policy_utilities.create_chosen_arm_features_info_spec(
time_step_spec.observation))
info_spec = policy_utilities.PerArmPolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
multiobjective_scalarized_predicted_rewards_mean=
scalarized_predicted_rewards_mean,
bandit_policy_type=bandit_policy_type,
chosen_arm_features=chosen_arm_features_info)
else:
info_spec = policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean,
multiobjective_scalarized_predicted_rewards_mean=
scalarized_predicted_rewards_mean,
bandit_policy_type=bandit_policy_type)
self._accepts_per_arm_features = accepts_per_arm_features
super(GreedyMultiObjectiveNeuralPolicy,
self).__init__(time_step_spec,
action_spec,
policy_state_spec=policy_state_spec,
clip=False,
info_spec=info_spec,
emit_log_probability='log_probability'
in emit_policy_info,
observation_and_action_constraint_splitter=(
observation_and_action_constraint_splitter),
name=name)
def _distribution(
self, time_step: ts.TimeStep,
policy_state: Sequence[types.TensorSpec]
) -> policy_step.PolicyStep:
observation = time_step.observation
if self.observation_and_action_constraint_splitter is not None:
observation, _ = self.observation_and_action_constraint_splitter(
observation)
predicted_objective_values_tensor, policy_state = self._predict(
observation, time_step.step_type, policy_state)
scalarized_reward = scalarize_objectives(
predicted_objective_values_tensor, self._scalarizer)
# Preserve static batch size values when they are available.
batch_size = (tf.compat.dimension_value(scalarized_reward.shape[0])
or tf.shape(scalarized_reward)[0])
mask = constraints.construct_mask_from_multiple_sources(
time_step.observation,
self._observation_and_action_constraint_splitter, (),
self._expected_num_actions)
# Argmax.
if mask is not None:
actions = policy_utilities.masked_argmax(
scalarized_reward, mask, output_type=self.action_spec.dtype)
else:
actions = tf.argmax(scalarized_reward,
axis=-1,
output_type=self.action_spec.dtype)
actions += self._action_offset
bandit_policy_values = tf.fill(
[batch_size, 1], policy_utilities.BanditPolicyType.GREEDY)
if self._accepts_per_arm_features:
# Saving the features for the chosen action to the policy_info.
def gather_observation(obs):
return tf.gather(params=obs, indices=actions, batch_dims=1)
chosen_arm_features = tf.nest.map_structure(
gather_observation,
observation[bandit_spec_utils.PER_ARM_FEATURE_KEY])
policy_info = policy_utilities.PerArmPolicyInfo(
log_probability=tf.zeros([batch_size], tf.float32)
if policy_utilities.InfoFields.LOG_PROBABILITY
in self._emit_policy_info else (),
predicted_rewards_mean=(
predicted_objective_values_tensor
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN
in self._emit_policy_info else ()),
multiobjective_scalarized_predicted_rewards_mean=(
scalarized_reward if policy_utilities.InfoFields.
MULTIOBJECTIVE_SCALARIZED_PREDICTED_REWARDS_MEAN
in self._emit_policy_info else ()),
bandit_policy_type=(
bandit_policy_values
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE
in self._emit_policy_info else ()),
chosen_arm_features=chosen_arm_features)
else:
policy_info = policy_utilities.PolicyInfo(
log_probability=tf.zeros([batch_size], tf.float32)
if policy_utilities.InfoFields.LOG_PROBABILITY
in self._emit_policy_info else (),
predicted_rewards_mean=(
predicted_objective_values_tensor
if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN
in self._emit_policy_info else ()),
multiobjective_scalarized_predicted_rewards_mean=(
scalarized_reward if policy_utilities.InfoFields.
MULTIOBJECTIVE_SCALARIZED_PREDICTED_REWARDS_MEAN
in self._emit_policy_info else ()),
bandit_policy_type=(
bandit_policy_values
if policy_utilities.InfoFields.BANDIT_POLICY_TYPE
in self._emit_policy_info else ()))
return policy_step.PolicyStep(
tfp.distributions.Deterministic(loc=actions), policy_state,
policy_info)
