# 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)

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(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

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

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

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

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.

property name(self)

Name of policy.

Returns

Name of policy

Return type

str

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.

property env_spec(self)

Policy environment specification.

Returns

Environment specification.

Return type

garage.EnvSpec

property observation_space(self)

Observation space.

Returns

The observation space of the environment.

Return type

akro.Space

property action_space(self)

Action space.

Returns

The action space of the environment.

Return type

akro.Space