garage.torch.policies.stochastic_policy
¶
Base Stochastic Policy.
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class
StochasticPolicy
(env_spec, name)¶ Bases:
garage.torch.policies.policy.Policy
,abc.ABC
Abstract base class for torch stochastic policies.
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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 a single action given an observation.
Parameters: observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\). Returns: - np.ndarray: Predicted action. Shape is
- \(env_spec.action_space\).
- dict:
- np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Standard deviation of logarithmic
- values of the distribution.
Return type: tuple
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get_actions
(self, observations)¶ Get actions given observations.
Parameters: observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\). Returns: - np.ndarray: Predicted actions.
- \(batch_dim \bullet env_spec.action_space\).
- dict:
- np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic
- values of the distribution.
Return type: tuple
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forward
(self, observations)¶ Compute the action distributions from the observations.
Parameters: observations (torch.Tensor) – Batch of observations on default torch device. Returns: Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors. Do not need to be detached, and can be on any device.Return type: torch.distributions.Distribution
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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
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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.
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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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