OnPolicySharedActorCritic

OnPolicySharedActorCritic(
   observation_space: gym.Space, action_space: gym.Space, feature_dim: int,
   hidden_dim: int = 512, opt_class: Type[th.optim.Optimizer] = th.optim.Adam,
   opt_kwargs: Optional[Dict[str, Any]] = None, aux_critic: bool = False,
   init_fn: str = 'orthogonal'
)

Actor-Critic network for on-policy algorithms like PPO and A2C.

Args

observation_space (gym.Space) : Observation space.
action_space (gym.Space) : Action space.
feature_dim (int) : Number of features accepted.
hidden_dim (int) : Number of units per hidden layer.
opt_class (Type[th.optim.Optimizer]) : Optimizer class.
opt_kwargs (Dict[str, Any]) : Optimizer keyword arguments.
aux_critic (bool) : Use auxiliary critic or not, for PPG agent.
init_fn (str) : Parameters initialization method.

Returns

Actor-Critic network instance.

Methods:

.describe

source

.describe()

Describe the policy.

.freeze

source

.freeze(
   encoder: nn.Module, dist: Distribution
)

Freeze all the elements like encoder and dist.

Args

encoder (nn.Module) : Encoder network.
dist (Distribution) : Distribution class.

Returns

None.

.forward

source

.forward(
   obs: th.Tensor, training: bool = True
)

Get actions and estimated values for observations.

Args

obs (th.Tensor) : Observations.
training (bool) : training mode, True or False.

Returns

Sampled actions, estimated values, and log of probabilities for observations when training is True, else only deterministic actions.

.get_value

source

.get_value(
   obs: th.Tensor
)

Get estimated values for observations.

Args

obs (th.Tensor) : Observations.

Returns

Estimated values.

.evaluate_actions

source

.evaluate_actions(
   obs: th.Tensor, actions: th.Tensor
)

Evaluate actions according to the current policy given the observations.

Args

obs (th.Tensor) : Sampled observations.
actions (th.Tensor) : Sampled actions.

Returns

Estimated values, log of the probability evaluated at actions, entropy of distribution.

.get_policy_outputs

source

.get_policy_outputs(
   obs: th.Tensor
)

Get policy outputs for training.

Args

obs (Tensor) : Observations.

Returns

Policy outputs like unnormalized probabilities for Discrete tasks.

.get_dist_and_aux_value

source

.get_dist_and_aux_value(
   obs: th.Tensor
)

Get probs and auxiliary estimated values for auxiliary phase update.

Args

obs : Sampled observations.

Returns

Sample distribution, estimated values, auxiliary estimated values.

.save

source

.save(
   path: Path, pretraining: bool, global_step: int
)

Save models.

Args

path (Path) : Save path.
pretraining (bool) : Pre-training mode.
global_step (int) : Global training step.

Returns

None.