`garage.np.policies.fixed_policy`¶

Policy that performs a fixed sequence of actions.

class FixedPolicy(env_spec, scripted_actions, agent_infos=None)¶

Policy that performs a fixed sequence of actions.

Parameters:	env_spec (garage.envs.env_spec.EnvSpec) – Environment specification. scripted_actions (list[np.ndarray] or np.ndarray) – Sequence of actions to perform. agent_infos (list[dict[str, np.ndarray]] or None) – Sequence of agent_infos to produce.

env_spec¶

Policy environment specification.

Returns:	Environment specification.
Return type:	garage.EnvSpec

name¶

Name of policy.

Returns:	Name of policy
Return type:	str

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

reset(self, do_resets=None)¶

Reset policy.

Parameters:	do_resets (None or list[bool]) – Vectorized policy states to reset.
Raises:	`ValueError` – If do_resets has length greater than 1.

set_param_values(self, params)¶

Set param values of policy.

Parameters:	params (object) – Ignored.

get_param_values(self)¶

Return policy params (there are none).

Returns:	Empty tuple.
Return type:	tuple

get_action(self, observation)¶

Get next action.

Parameters:	observation (np.ndarray) – Ignored.
Raises:	`ValueError` – If policy is currently vectorized (reset was called with more than one done value).
Returns:	The action and agent_info for this time step.
Return type:	tuple[np.ndarray, dict[str, np.ndarray]]

get_actions(self, observations)¶

Get next action.

Parameters:	observations (np.ndarray) – Ignored.
Raises:	`ValueError` – If observations has length greater than 1.
Returns:	The action and agent_info for this time step.
Return type:	tuple[np.ndarray, dict[str, np.ndarray]]

garage.np.policies.fixed_policy¶