garage.tf.policies.uniform_control_policy
¶
Uniform control policy.
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class
UniformControlPolicy
(env_spec)¶ Bases:
garage.tf.policies.policy.Policy
Policy that output random action uniformly.
Parameters: env_spec (garage.envs.env_spec.EnvSpec) – Environment specification. -
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]
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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]
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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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get_action
(self, observation)¶ Get single action from this policy for the input observation.
Parameters: observation (numpy.ndarray) – Observation from environment. Returns: Action dict: Predicted action and agent information. It returns an empty dict since there is no parameterization.Return type: numpy.ndarray
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get_actions
(self, observations)¶ Get multiple actions from this policy for the input observations.
Parameters: observations (numpy.ndarray) – Observations from environment. Returns: Actions dict: Predicted action and agent information. It returns an empty dict since there is no parameterization.Return type: numpy.ndarray
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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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