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.
- 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).
- property env_spec¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- forward(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
- get_action(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
- get_actions(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
- get_param_values()¶
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
- set_param_values(state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(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.