tf_agents.bandits.policies.ranking_policy.RankingPolicy

A class implementing ranking policies in TF Agents.

Inherits From: TFPolicy

tf_agents.bandits.policies.ranking_policy.RankingPolicy(
    num_items: int,
    num_slots: int,
    time_step_spec: tf_agents.typing.types.TimeStep,
    network: tf_agents.typing.types.Network,
    item_sampler: tfd.Distribution,
    penalty_mixture_coefficient: float = 1.0,
    logits_temperature: float = 1.0,
    name: Optional[Text] = None
)

The ranking policy needs at initialization the number of items per round to rank, a scorer network, and a score penalizer function. This function should ensure that similar items don't all get high scores and thus a diverse set of items is recommended.

In the case the number of items to rank varies from iteration to iteration, the observation contains a num_actions value, that specifies the number of items available. Note that in this case it can happen that the number of ranked items is less than the number of slots. Thus, consumers of the output of policy.action should always use the num_actions value to determine what part of the output is the action ranking.

If num_actions field is not used, the policy is always presented with num_items many items, which should be greater than or equal to num_slots.

num_items The number of items the policy can choose from, to be slotted.
num_slots The number of recommendation slots presented to the user, i.e., chosen by the policy.
time_step_spec The time step spec.
network The network that estimates scores of items, given global and item features.
item_sampler A distibution that takes scores and item features, and samples an ordered list of num_slots items. Similarity penalization can be implemented within this sampler.
penalty_mixture_coefficient A parameter responsible for the balance between selecting high scoring items and enforcing diverisity.
logits_temperature The "temperature" parameter for sampling. All the logits will be divided by this float value. This value must be positive.
name The name of this policy instance.

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_slots

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.