garage.np.policies.scripted_policy
¶
Simulates a garage policy object.
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class
ScriptedPolicy
(scripted_actions, agent_env_infos=None)¶ Bases:
garage.np.policies.policy.Policy
Simulates a garage policy object.
Parameters: -
env_spec
¶ Policy environment specification.
Returns: Environment specification. Return type: garage.EnvSpec
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observation_space
¶ Observation space.
Returns: The observation space of the environment. Return type: akro.Space
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action_space
¶ Action space.
Returns: The action space of the environment. Return type: akro.Space
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set_param_values
(self, params)¶ Set param values.
Parameters: params (np.ndarray) – A numpy array of parameter values.
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get_param_values
(self)¶ Get param values.
Returns: - Values of the parameters evaluated in
- the current session
Return type: np.ndarray
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get_action
(self, observation)¶ Return a single action.
Parameters: observation (numpy.ndarray) – Observations. Returns: Action given input observation. dict[dict]: Agent infos indexed by observation. Return type: int
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get_actions
(self, observations)¶ Return multiple actions.
Parameters: observations (numpy.ndarray) – Observations. Returns: Actions given input observations. dict[dict]: Agent info indexed by observation. Return type: list[int]
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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.
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