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.

name

Name of policy.

Returns:Name of policy
Return type:str
env_spec

Policy environment specification.

Returns:Environment specification.
Return type:garage.EnvSpec
observation_space

Observation space.

Returns:The observation space of the environment.
Return type:akro.Space
action_space

Action space.

Returns:The action space of the environment.
Return type:akro.Space
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
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.
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.