class torchrl.modules.TanhNormal(loc: Tensor, scale: Tensor, upscale: Union[Tensor, Number] = 5.0, min: Union[Tensor, Number] = - 1.0, max: Union[Tensor, Number] = 1.0, event_dims: int = 1, tanh_loc: bool = False)[source]

Implements a TanhNormal distribution with location scaling.

Location scaling prevents the location to be “too far” from 0 when a TanhTransform is applied, but ultimately leads to numerically unstable samples and poor gradient computation (e.g. gradient explosion). In practice, with location scaling the location is computed according to

\[loc = tanh(loc / upscale) * upscale.\]
  • loc (torch.Tensor) – normal distribution location parameter

  • scale (torch.Tensor) – normal distribution sigma parameter (squared root of variance)

  • upscale (torch.Tensor or number) –

    ‘a’ scaling factor in the formula:

    \[loc = tanh(loc / upscale) * upscale.\]

  • min (torch.Tensor or number, optional) – minimum value of the distribution. Default is -1.0;

  • max (torch.Tensor or number, optional) – maximum value of the distribution. Default is 1.0;

  • event_dims (int, optional) – number of dimensions describing the action. Default is 1;

  • tanh_loc (bool, optional) – if True, the above formula is used for the location scaling, otherwise the raw value is kept. Default is False;

property mode

Returns the mode of the distribution.


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