garage.torch.policies.deterministic_mlp_policy
¶
This modules creates a deterministic policy network.
A neural network can be used as policy method in different RL algorithms. It accepts an observation of the environment and predicts an action.
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class
DeterministicMLPPolicy
(env_spec, name='DeterministicMLPPolicy', **kwargs)¶ Bases:
garage.torch.policies.policy.Policy
Implements a deterministic policy network.
The policy network selects action based on the state of the environment. It uses a PyTorch neural network module to fit the function of pi(s).
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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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forward
(self, observations)¶ Compute actions from the observations.
Parameters: observations (torch.Tensor) – Batch of observations on default torch device. Returns: Batch of actions. Return type: torch.Tensor
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get_action
(self, observation)¶ Get a single action given an observation.
Parameters: observation (np.ndarray) – Observation from the environment. Returns: - np.ndarray: Predicted action.
- dict:
- np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Log of standard deviation 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. Returns: - np.ndarray: Predicted actions.
- dict:
- np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Log of standard deviation of the
- distribution
Return type: tuple
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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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