BasePolicy

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

Base class for all policies.

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

Base policy instance.

Methods:

.optimizers

source

.optimizers()

Get optimizers.

.describe

source

.describe()

Describe the policy.

.describe

source

.describe()

Describe the policy.

.forward

source

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

Forward method.

Args

obs (th.Tensor) : Observation from the environment.
training (bool) : Whether the agent is being trained or not.

Returns

Sampled actions, estimated values, ..., depends on specific algorithms.

.freeze

source

.freeze(
   *args, **kwargs
)

Freeze the policy and start training.

.save

source

.save(
   *args, **kwargs
)

Save models.

.load

source

.load(
   path: str, device: th.device
)

Load initial parameters.

Args

path (str) : Import path.
device (th.device) : Device to use.

Returns

None.