tf_agents.policies.py_tf_eager_policy.PyTFEagerPolicy

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Exposes a numpy API for TF policies in Eager mode.

Inherits From: PyTFEagerPolicyBase

tf_agents.policies.py_tf_eager_policy.PyTFEagerPolicy(
    *args, **kwargs
)

Args:

• time_step_spec: A TimeStep ArraySpec of the expected time_steps. Usually provided by the user to the subclass.
• action_spec: A nest of BoundedArraySpec representing the actions. Usually provided by the user to the subclass.
• policy_state_spec: A nest of ArraySpec representing the policy state. Provided by the subclass, not directly by the user.
• info_spec: A nest of ArraySpec representing the policy info. Provided by the subclass, not directly by the user.
• observation_and_action_constraint_splitter: A function used to process observations with action constraints. These constraints can indicate, for example, a mask of valid/invalid actions for a given 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 constraint. An example observation_and_action_constraint_splitter could be as simple as:

def observation_and_action_constraint_splitter(observation):
  return observation['network_input'], observation['constraint']

Note: when using observation_and_action_constraint_splitter, make sure the provided q_network is compatible with the network-specific half of the output of the observation_and_action_constraint_splitter. In particular, observation_and_action_constraint_splitter will be called on the observation before passing to the network. If observation_and_action_constraint_splitter is None, action constraints are not applied.

Attributes:

• action_spec: Describes the ArraySpecs of the np.Array returned by action().

  action can be a single np.Array, or a nested dict, list or tuple of np.Array.

• info_spec: Describes the Arrays emitted as info by action().

• observation_and_action_constraint_splitter

• policy_state_spec: Describes the arrays expected by functions with policy_state as input.

• policy_step_spec: Describes the output of action().

• time_step_spec: Describes the TimeStep np.Arrays expected by action(time_step).

• trajectory_spec: Describes the data collected when using this policy with an environment.

Methods

action

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action(
    time_step, policy_state=()
)

Generates next action given the time_step and policy_state.

Args:

• time_step: A TimeStep tuple corresponding to time_step_spec().
• policy_state: An optional previous policy_state.

Returns:

A PolicyStep named tuple containing: action: A nest of action Arrays matching the action_spec(). state: A nest of policy states to be fed into the next call to action. info: Optional side information such as action log probabilities.

get_initial_state

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get_initial_state(
    batch_size=None
)

Returns an initial state usable by the policy.

Args:

• batch_size: An optional batch size.

Returns:

An initial policy state.