reference/generated/torchrl.modules.NoisyLazyLinear

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NoisyLazyLinear

class torchrl.modules.NoisyLazyLinear(out_features: int, bias: bool = True, device: device | str | int | None = None, dtype: dtype | None = None, std_init: float = 0.5, use_exploration_type: bool | None = True)

Noisy Lazy Linear Layer.

This class makes the Noisy Linear layer lazy, in that the in_feature argument does not need to be passed at initialization (but is inferred after the first call to the layer).

For more context on noisy layers, see the NoisyLinear class. Like NoisyLinear, noise is sampled only by reset_noise() (at materialization) and must be reapplied by the caller.

Parameters:

• out_features (int) – out features dimension

• bias (bool, optional) – if True, a bias term will be added to the matrix multiplication: Ax + b. Defaults to True.

• device (DEVICE_TYPING, optional) – device of the layer. Defaults to "cpu".

• dtype (torch.dtype, optional) – dtype of the parameters. Defaults to the default PyTorch dtype.

• std_init (scalar) – initial value of the Gaussian standard deviation before optimization. Defaults to 0.5 as per the original paper.

• use_exploration_type (bool or None, optional) – if True, noise is controlled by exploration_type(). If False, noise is controlled by self.training (legacy behavior). If None, it is treated as True. Defaults to True.

cls_to_become

alias of NoisyLinear

initialize_parameters(input: Tensor) → None

Initialize parameters according to the input batch properties.

This adds an interface to isolate parameter initialization from the forward pass when doing parameter shape inference.

reset_noise() → None

Resample the factorized Gaussian noise buffers.

Called from __init__(). The forward pass does not call this method; apply it after each optimization step (for example module.apply(reset_noise)).

reset_parameters() → None

Resets parameters based on their initialization used in __init__.