rofunc.learning.RofuncRL.processors.standard_scaler#

1.  Module Contents#

1.1.  Classes#

RunningStandardScaler

1.2.  Functions#

empty_preprocessor

1.3.  API#

class rofunc.learning.RofuncRL.processors.standard_scaler.RunningStandardScaler(size: Union[int, Tuple[int], gym.Space, gymnasium.Space], epsilon: float = 1e-08, clip_threshold: float = 5.0, device: Optional[Union[str, torch.device]] = None)[source]#

Bases: torch.nn.Module

forward(x: torch.Tensor, train: bool = False, inverse: bool = False, no_grad: bool = True) → torch.Tensor[source]#

Forward pass of the standardizer

Example:

>>> x = torch.rand(3, 2, device="cuda:0")
>>> running_standard_scaler(x)
tensor([[0.6933, 0.1905],
        [0.3806, 0.3162],
        [0.1140, 0.0272]], device='cuda:0')

>>> running_standard_scaler(x, train=True)
tensor([[ 0.8681, -0.6731],
        [ 0.0560, -0.3684],
        [-0.6360, -1.0690]], device='cuda:0')

>>> running_standard_scaler(x, inverse=True)
tensor([[0.6260, 0.5468],
        [0.5056, 0.5987],
        [0.4029, 0.4795]], device='cuda:0')
Parameters:

  • x (torch.Tensor) – Input tensor

  • train (bool, optional) – Whether to train the standardizer (default: False)

  • inverse (bool, optional) – Whether to inverse the standardizer to scale back the data (default: False)

  • no_grad (bool, optional) – Whether to disable the gradient computation (default: True)

rofunc.learning.RofuncRL.processors.standard_scaler.empty_preprocessor(_input, *args, **kwargs)[source]#