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tf_agents.policies.scripted_py_policy.ScriptedPyPolicy

View source on GitHub

Returns actions from the given configuration.

Inherits From: Base

tf_agents.policies.scripted_py_policy.ScriptedPyPolicy(
    time_step_spec, action_spec, action_script
)

Used in the notebooks

Used in the tutorials

Args:

  • time_step_spec: A time_step_spec for the policy will interact with.
  • action_spec: An action_spec for the environment the policy will interact with.
  • action_script: A list of 2-tuples of the form (n, nest) where the nest of actions follow the action_spec. Each action will be executed for n steps.

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.

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

  • 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

View source

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

View source

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.