tf_agents.policies.py_tf_eager_policy.SavedModelPyTFEagerPolicy

Exposes a numpy API for saved_model policies in Eager mode.

Inherits From: PyTFEagerPolicyBase

tf_agents.policies.py_tf_eager_policy.SavedModelPyTFEagerPolicy(
    model_path: Text,
    time_step_spec: Optional[tf_agents.trajectories.time_step.TimeStep] = None,
    action_spec: Optional[types.NestedTensorSpec] = None,
    policy_state_spec: tf_agents.typing.types.NestedTensorSpec = (),
    info_spec: tf_agents.typing.types.NestedTensorSpec = (),
    load_specs_from_pbtxt: bool = False
)

Used in the notebooks

Used in the tutorials

model_path Path to a saved_model generated by the policy_saver.
time_step_spec Optional nested structure of ArraySpecs describing the policy's time_step_spec. This is not used by the SavedModelPyTFEagerPolicy, but may be accessed by other objects as it is part of the public policy API.
action_spec Optional nested structure of ArraySpecs describing the policy's action_spec. This is not used by the SavedModelPyTFEagerPolicy, but may be accessed by other objects as it is part of the public policy API.
policy_state_spec Optional nested structure of ArraySpecs describing the policy's policy_state_spec. This is not used by the SavedModelPyTFEagerPolicy, but may be accessed by other objects as it is part of the public policy API.
info_spec Optional nested structure of ArraySpecs describing the policy's info_spec. This is not used by the SavedModelPyTFEagerPolicy, but may be accessed by other objects as it is part of the public policy API.
load_specs_from_pbtxt If True the specs will be loaded from the proto file generated by the policy_saver.

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: tf_agents.trajectories.time_step.TimeStep,
    policy_state: tf_agents.typing.types.NestedArray = ()
) -> tf_agents.trajectories.policy_step.PolicyStep

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: Optional[int] = None
) -> tf_agents.typing.types.NestedArray

Returns an initial state usable by the policy.

Args
batch_size An optional batch size.

Returns
An initial policy state.

get_metadata

View source

get_metadata()

Returns the metadata of the saved model.

get_train_step

View source

get_train_step() -> tf_agents.typing.types.Int

Returns the training global step of the saved model.

update_from_checkpoint

View source

update_from_checkpoint(
    checkpoint_path: Text
)

Allows users to update saved_model variables directly from a checkpoint.

checkpoint_path is a path that was passed to either PolicySaver.save() or PolicySaver.save_checkpoint(). The policy looks for set of checkpoint files with the file prefix `/variables/variables'

Args
checkpoint_path Path to the checkpoint to restore and use to udpate this policy.

variables

View source

variables()