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OnPolicyDecoupledActorCritic

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OnPolicyDecoupledActorCritic(
   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, init_fn: str = 'orthogonal'
)

Actor-Critic network for on-policy algorithms like DAAC.

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.
  • init_fn (str) : Parameters initialization method.

Returns

Actor-Critic network instance.

Methods:

.describe

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.describe()

Describe the policy.

.freeze

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.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

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.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

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.get_value(
   obs: th.Tensor
)

Get estimated values for observations.

Args

  • obs (th.Tensor) : Observations.

Returns

Estimated values.

.evaluate_actions

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.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.

.save

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.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.