actor#
Source code: tianshou/highlevel/module/actor.py
- class ActorFactory[source]#
- abstract create_module(envs: Environments, device: str | device) BaseActor | Module [source]#
- create_module_opt(envs: Environments, device: str | device, optim_factory: OptimizerFactory, lr: float) ModuleOpt [source]#
Creates the actor module along with its optimizer for the given learning rate.
- Parameters:
envs – the environments
device – the torch device
optim_factory – the optimizer factory
lr – the learning rate
- Returns:
a container with the actor module and its optimizer
- class ActorFactoryContinuous[source]#
Serves as a type bound for actor factories that are suitable for continuous action spaces.
- class ActorFactoryContinuousDeterministicNet(hidden_sizes: ~collections.abc.Sequence[int], activation: type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.ReLU'>)[source]#
- create_module(envs: Environments, device: str | device) BaseActor [source]#
- class ActorFactoryContinuousGaussianNet(hidden_sizes: ~collections.abc.Sequence[int], unbounded: bool = True, conditioned_sigma: bool = False, activation: type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.ReLU'>)[source]#
- create_module(envs: Environments, device: str | device) BaseActor [source]#
- class ActorFactoryDefault(continuous_actor_type: ~tianshou.highlevel.module.actor.ContinuousActorType, hidden_sizes: ~collections.abc.Sequence[int] = (64, 64), hidden_activation: type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.ReLU'>, continuous_unbounded: bool = False, continuous_conditioned_sigma: bool = False, discrete_softmax: bool = True)[source]#
An actor factory which, depending on the type of environment, creates a suitable MLP-based policy.
- DEFAULT_HIDDEN_SIZES = (64, 64)#
- create_module(envs: Environments, device: str | device) BaseActor [source]#
- class ActorFactoryDiscreteNet(hidden_sizes: ~collections.abc.Sequence[int], softmax_output: bool = True, activation: type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.ReLU'>)[source]#
- create_module(envs: Environments, device: str | device) BaseActor [source]#
- class ActorFactoryTransientStorageDecorator(actor_factory: ActorFactory, actor_future: ActorFuture)[source]#
Wraps an actor factory, storing the most recently created actor instance such that it can be retrieved.
- create_module(envs: Environments, device: str | device) BaseActor | Module [source]#
- class ActorFuture(actor: BaseActor | Module | None = None)[source]#
Container, which, in the future, will hold an actor instance.
- class ActorFutureProviderProtocol(*args, **kwargs)[source]#
- get_actor_future() ActorFuture [source]#
- class ContinuousActorType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
- DETERMINISTIC = 'deterministic'#
- GAUSSIAN = 'gaussian'#
- UNSUPPORTED = 'unsupported'#
- class IntermediateModuleFactoryFromActorFactory(actor_factory: ActorFactory)[source]#
- create_intermediate_module(envs: Environments, device: str | device) IntermediateModule [source]#