garage.torch.policies.policy

Base Policy.

class Policy(env_spec, name)

Bases: torch.nn.Module, garage.np.policies.Policy, abc.ABC

Inheritance diagram of garage.torch.policies.policy.Policy

Policy base class.

Parameters:
  • env_spec (EnvSpec) – Environment specification.
  • name (str) – Name of policy.
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]]
get_param_values(self)

Get the parameters to the policy.

This method is included to ensure consistency with TF policies.

Returns:The parameters (in the form of the state dictionary).
Return type:dict
set_param_values(self, state_dict)

Set the parameters to the policy.

This method is included to ensure consistency with TF policies.

Parameters:state_dict (dict) – State dictionary.
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.