garage.tf.policies.policy

Base class for policies in TensorFlow.

class Policy

Bases: garage.np.policies.Policy

Base class for policies in TensorFlow.

state_info_specs

State info specification.

Returns:

keys and shapes for the information related to the

module’s state when taking an action.

Return type:List[str]

state_info_keys

State info keys.

Returns:

keys for the information related to the module’s state

when taking an input.

Return type:List[str]

name

Name of policy.

Returns:Name of policy Return type:str

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

observation_space

Observation space.

Returns:The observation space of the environment. Return type:akro.Space

action_space

Action space.

Returns:The action space of the environment. Return type:akro.Space

get_action(self, observation)

Get action sampled from the policy.

Parameters:observation (np.ndarray) – Observation from the environment. Returns:

Action and extra agent

info.

Return type:Tuple[np.ndarray, dict[str,np.ndarray]]

get_actions(self, observations)

Get actions given observations.

Parameters:observations (np.ndarray) – Observations from the environment. Returns:

Actions and extra agent

infos.

Return type:Tuple[np.ndarray, dict[str,np.ndarray]]

reset(self, do_resets=None)

Reset the policy.

This is effective only to recurrent policies.

do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs, i.e. batch size.

Parameters:do_resets (numpy.ndarray) – Bool array indicating which states to be reset.