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.

class DeterministicMLPPolicy(env_spec, name='DeterministicMLPPolicy', **kwargs)[source]¶

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).

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

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

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