garage.torch.policies.stochastic_policy

Base Stochastic Policy.

class StochasticPolicy(env_spec, name)

Bases: garage.torch.policies.policy.Policy, abc.ABC

Inheritance diagram of garage.torch.policies.stochastic_policy.StochasticPolicy

Abstract base class for torch stochastic policies.

get_action(self, observation)

Get a single action given an observation.

Parameters

observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\).

Returns

  • np.ndarray: Predicted action. Shape is

    \(env_spec.action_space\).

  • dict:
    • np.ndarray[float]: Mean of the distribution

    • np.ndarray[float]: Standard deviation of logarithmic

      values of the distribution.

Return type

tuple

get_actions(self, observations)

Get actions given observations.

Parameters

observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\).

Returns

  • np.ndarray: Predicted actions.

    \(batch_dim \bullet env_spec.action_space\).

  • dict:
    • np.ndarray[float]: Mean of the distribution.

    • np.ndarray[float]: Standard deviation of logarithmic

      values of the distribution.

Return type

tuple

abstract forward(self, observations)

Compute the action distributions from the observations.

Parameters

observations (torch.Tensor) – Batch of observations on default torch device.

Returns

Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors.

Do not need to be detached, and can be on any device.

Return type

torch.distributions.Distribution

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