tf_agents.policies.py_tf_eager_policy.PyTFEagerPolicy

Exposes a numpy API for TF policies in Eager mode.

tf_agents.policies.py_tf_eager_policy.PyTFEagerPolicy(
    policy, use_tf_function=False
)

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.
`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(
    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(
    batch_size=None
)

Returns an initial state usable by the policy.

Args
`batch_size`	An optional batch size.

Returns
An initial policy state.

variables()