stable_learning_control.algos.pytorch.policies.lyapunov_actor_critic

Lyapunov (soft) actor critic policy.

This module contains a Pytorch implementation of the Lyapunov Actor Critic policy of Han et al. 2020.

Attributes

`HIDDEN_SIZES_DEFAULT`
`ACTIVATION_DEFAULT`
`OUTPUT_ACTIVATION_DEFAULT`

Classes

LyapunovActorCritic

Lyapunov (soft) Actor-Critic network.

Module Contents

stable_learning_control.algos.pytorch.policies.lyapunov_actor_critic.HIDDEN_SIZES_DEFAULT[source]

stable_learning_control.algos.pytorch.policies.lyapunov_actor_critic.ACTIVATION_DEFAULT[source]

stable_learning_control.algos.pytorch.policies.lyapunov_actor_critic.OUTPUT_ACTIVATION_DEFAULT[source]

class stable_learning_control.algos.pytorch.policies.lyapunov_actor_critic.LyapunovActorCritic(observation_space, action_space, hidden_sizes=HIDDEN_SIZES_DEFAULT, activation=ACTIVATION_DEFAULT, output_activation=OUTPUT_ACTIVATION_DEFAULT)[source]

Bases: torch.nn.Module

Lyapunov (soft) Actor-Critic network.

self.pi

The squashed gaussian policy network (actor).

Type:: SquashedGaussianActor

self.L

The soft L-network (critic).

Type:: LCritic

Initialise the LyapunovActorCritic object.

Parameters:

observation_space (gym.space.box.Box) – A gymnasium observation space.
action_space (gym.space.box.Box) – A gymnasium action space.
hidden_sizes (Union[dict, tuple, list], optional) – Sizes of the hidden layers for the actor. Defaults to (256, 256).
activation (Union[dict, torch.nn.modules.activation], optional) – The (actor and critic) hidden layers activation function. Defaults to torch.nn.ReLU.
output_activation (Union[dict, torch.nn.modules.activation], optional) – The (actor and critic) output activation function. Defaults to torch.nn.ReLU for the actor and nn.Identity for the critic.

Note

It is currently not possible to set the critic output activation function when using the LyapunovActorCritic. This is since it by design requires the critic output activation to by of type torch.square().

obs_dim[source]

act_dim[source]

act_limits[source]

pi[source]

L[source]

forward(obs, act, deterministic=False, with_logprob=True)[source]

Performs a forward pass through all the networks (Actor and L critic).

Parameters:

obs (torch.Tensor) – The tensor of observations.
act (torch.Tensor) – The tensor of actions.
deterministic (bool, optional) – Whether we want to use a deterministic policy (used at test time). When true the mean action of the stochastic policy is returned. If false the action is sampled from the stochastic policy. Defaults to False.
with_logprob (bool, optional) – Whether we want to return the log probability of an action. Defaults to True.

Returns:

tuple containing:

pi_action (torch.Tensor): The actions given by the policy.

logp_pi (torch.Tensor): The log probabilities of each of these actions.

L (torch.Tensor): Critic L values.

Return type:

(tuple)

Note

Useful for when you want to print out the full network graph using TensorBoard.

act(obs, deterministic=False)[source]

Returns the action from the current state given the current policy.

Parameters:

obs (torch.Tensor) – The current observation (state).
deterministic (bool, optional) – Whether we want to use a deterministic policy (used at test time). When true the mean action of the stochastic policy is returned. If False the action is sampled from the stochastic policy. Defaults to False.

Returns:

The action from the current state given the current policy.

Return type:

numpy.ndarray