tf_agents.bandits.policies.falcon_reward_prediction_policy.FalconRewardPredictionPolicy

Policy that samples actions based on the FALCON algorithm.

Inherits From: RewardPredictionBasePolicy, TFPolicy

tf_agents.bandits.policies.falcon_reward_prediction_policy.FalconRewardPredictionPolicy(
    time_step_spec: tf_agents.typing.types.TimeStep,
    action_spec: tf_agents.typing.types.NestedTensorSpec,
    reward_network: tf_agents.typing.types.Network,
    exploitation_coefficient: Optional[types.FloatOrReturningFloat] = 1.0,
    max_exploration_probability_hint: Optional[types.FloatOrReturningFloat] = None,
    observation_and_action_constraint_splitter: Optional[types.Splitter] = None,
    accepts_per_arm_features: bool = False,
    constraints: Iterable[tf_agents.bandits.policies.constraints.BaseConstraint] = (),
    emit_policy_info: Tuple[Text, ...] = (),
    num_samples_list: Sequence[tf.Variable] = (),
    name: Optional[Text] = None
)

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).
exploitation_coefficient float or callable that returns a float. Its value will be internally lower-bounded at 0. It controls how exploitative the policy behaves w.r.t the predicted rewards: A larger value makes the policy sample the greedy action (one with the best predicted reward) with a higher probability.
max_exploration_probability_hint An optional float, representing a hint on the maximum exploration probability, internally clipped to [0, 1]. When this argument is set, exploitation_coefficient is ignored and the policy attempts to choose non-greedy actions with at most this probability. When such an upper bound cannot be achieved, e.g. due to insufficient training data, the policy attempts to minimize the probability of choosing non-greedy actions on a best-effort basis. For a demonstration of how it affects the policy behavior, see the unit test testMaxExplorationProbabilityHint in falcon_reward_prediction_policy_test.
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.BaseConstraint.
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.
num_samples_list Sequence of tf.Variable's representing the number of examples for every action that the policy was trained with. For per-arm features, the size of the list is expected to be 1, representing the total number of examples the policy was trained with.
name The name of this policy. All variables in this module will fall under that name. Defaults to the class name.

NotImplementedError If action_spec contains more than one BoundedTensorSpec or the BoundedTensorSpec is not valid.

accepts_per_arm_features

action_spec Describes the TensorSpecs of the Tensors expected by step(action).

action can be a single Tensor, or a nested dict, list or tuple of Tensors.

collect_data_spec Describes the Tensors written when using this policy with an environment.
emit_log_probability Whether this policy instance emits log probabilities or not.
info_spec Describes the Tensors emitted as info by action and distribution.

info can be an empty tuple, a single Tensor, or a nested dict, list or tuple of Tensors.

num_samples_list

num_trainable_elements

observation_and_action_constraint_splitter

policy_state_spec Describes the Tensors expected by step(_, policy_state).

policy_state can be an empty tuple, a single Tensor, or a nested dict, list or tuple of Tensors.

policy_step_spec Describes the output of action().
time_step_spec Describes the TimeStep tensors returned by step().
trajectory_spec Describes the Tensors written when using this policy with an environment.
validate_args Whether action & distribution validate input and output args.

Methods

action

View source

action(
    time_step: tf_agents.trajectories.TimeStep,
    policy_state: tf_agents.typing.types.NestedTensor = (),
    seed: Optional[types.Seed] = None
) -> tf_agents.trajectories.PolicyStep

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.

distribution

View source

distribution(
    time_step: tf_agents.trajectories.TimeStep,
    policy_state: tf_agents.typing.types.NestedTensor = ()
) -> tf_agents.trajectories.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.

get_initial_state

View source

get_initial_state(
    batch_size: Optional[types.Int]
) -> tf_agents.typing.types.NestedTensor

Returns an initial state usable by the policy.

Args
batch_size Tensor or constant: size of the batch dimension. Can be None in which case no dimensions gets added.

Returns
A nested object of type policy_state containing properly initialized Tensors.

update

View source

update(
    policy,
    tau: float = 1.0,
    tau_non_trainable: Optional[float] = None,
    sort_variables_by_name: bool = False
) -> tf.Operation

Update the current policy with another policy.

This would include copying the variables from the other policy.

Args
policy Another policy it can update from.
tau A float scalar in [0, 1]. When tau is 1.0 (the default), we do a hard update. This is used for trainable variables.
tau_non_trainable A float scalar in [0, 1] for non_trainable variables. If None, will copy from tau.
sort_variables_by_name A bool, when True would sort the variables by name before doing the update.

Returns
An TF op to do the update.