garage.torch.policies.deterministic_mlp_policy module¶
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)[source]¶ 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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forward
(observations)[source]¶ 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
(observation)[source]¶ Get a single action given an observation.
Parameters: observation (np.ndarray) – Observation from the environment. Returns: - np.ndarray: Predicted action.
- dict:
- list[float]: Mean of the distribution
- list[float]: Log of standard deviation of the
- distribution
Return type: tuple
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get_actions
(observations)[source]¶ Get actions given observations.
Parameters: observations (np.ndarray) – Observations from the environment. Returns: - np.ndarray: Predicted actions.
- dict:
- list[float]: Mean of the distribution
- list[float]: Log of standard deviation of the
- distribution
Return type: tuple
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