garage.np.policies
¶
Policies which use NumPy as a numerical backend.
-
class
FixedPolicy
(env_spec, scripted_actions, agent_infos=None)[source]¶ Bases:
garage.np.policies.policy.Policy
Policy that performs a fixed sequence of actions.
- Parameters
-
reset
(self, do_resets=None)[source]¶ 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)[source]¶ Set param values of policy.
- Parameters
params (object) – Ignored.
-
get_param_values
(self)[source]¶ Return policy params (there are none).
- Returns
Empty tuple.
- Return type
-
get_action
(self, observation)[source]¶ 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
-
get_actions
(self, observations)[source]¶ 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
-
property
env_spec
(self)¶ Policy environment specification.
- Returns
Environment specification.
- Return type
garage.EnvSpec
-
property
observation_space
(self)¶ Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
-
property
action_space
(self)¶ Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
-
class
Policy
[source]¶ Bases:
abc.ABC
Base class for policies based on numpy.
-
reset
(self, do_resets=None)[source]¶ 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.
-
property
env_spec
(self)¶ Policy environment specification.
- Returns
Environment specification.
- Return type
garage.EnvSpec
-
property
observation_space
(self)¶ Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
-
property
action_space
(self)¶ Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
-
-
class
ScriptedPolicy
(scripted_actions, agent_env_infos=None)[source]¶ Bases:
garage.np.policies.policy.Policy
Simulates a garage policy object.
- Parameters
-
set_param_values
(self, params)[source]¶ Set param values.
- Parameters
params (np.ndarray) – A numpy array of parameter values.
-
get_param_values
(self)[source]¶ Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
-
get_action
(self, observation)[source]¶ Return a single action.
- Parameters
observation (numpy.ndarray) – Observations.
- Returns
Action given input observation. dict[dict]: Agent infos indexed by observation.
- Return type
-
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.
-
property
env_spec
(self)¶ Policy environment specification.
- Returns
Environment specification.
- Return type
garage.EnvSpec
-
property
observation_space
(self)¶ Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
-
property
action_space
(self)¶ Action space.
- Returns
The action space of the environment.
- Return type
akro.Space