ObservationNorm¶
- class torchrl.envs.transforms.ObservationNorm(loc: Optional[float, torch.Tensor] = None, scale: Optional[float, torch.Tensor] = None, in_keys: Sequence[NestedKey] | None = None, out_keys: Sequence[NestedKey] | None = None, in_keys_inv: Sequence[NestedKey] | None = None, out_keys_inv: Sequence[NestedKey] | None = None, standard_normal: bool = False)[source]¶
Observation affine transformation layer.
Normalizes an observation according to
\[obs = obs * scale + loc\]- Parameters:
loc (number or tensor) – location of the affine transform
scale (number or tensor) – scale of the affine transform
in_keys (sequence of NestedKey, optional) – entries to be normalized. Defaults to [“observation”, “pixels”]. All entries will be normalized with the same values: if a different behaviour is desired (e.g. a different normalization for pixels and states) different
ObservationNorm
objects should be used.out_keys (sequence of NestedKey, optional) – output entries. Defaults to the value of in_keys.
in_keys_inv (sequence of NestedKey, optional) – ObservationNorm also supports inverse transforms. This will only occur if a list of keys is provided to
in_keys_inv
. If none is provided, only the forward transform will be called.out_keys_inv (sequence of NestedKey, optional) – output entries for the inverse transform. Defaults to the value of in_keys_inv.
standard_normal (bool, optional) –
if
True
, the transform will be\[obs = (obs-loc)/scale\]as it is done for standardization. Default is False.
Examples
>>> torch.set_default_tensor_type(torch.DoubleTensor) >>> r = torch.randn(100, 3)*torch.randn(3) + torch.randn(3) >>> td = TensorDict({'obs': r}, [100]) >>> transform = ObservationNorm( ... loc = td.get('obs').mean(0), ... scale = td.get('obs').std(0), ... in_keys=["obs"], ... standard_normal=True) >>> _ = transform(td) >>> print(torch.isclose(td.get('obs').mean(0), ... torch.zeros(3)).all()) tensor(True) >>> print(torch.isclose(td.get('next_obs').std(0), ... torch.ones(3)).all()) tensor(True)
The normalization stats can be automatically computed: .. rubric:: Examples
>>> from torchrl.envs.libs.gym import GymEnv >>> torch.manual_seed(0) >>> env = GymEnv("Pendulum-v1") >>> env = TransformedEnv(env, ObservationNorm(in_keys=["observation"])) >>> env.set_seed(0) >>> env.transform.init_stats(100) >>> print(env.transform.loc, env.transform.scale) tensor([-1.3752e+01, -6.5087e-03, 2.9294e-03], dtype=torch.float32) tensor([14.9636, 2.5608, 0.6408], dtype=torch.float32)
- init_stats(num_iter: int, reduce_dim: Union[int, Tuple[int]] = 0, cat_dim: Optional[int] = None, key: Optional[Union[str, Tuple[str, ...]]] = None, keep_dims: Optional[Tuple[int]] = None) None [source]¶
Initializes the loc and scale stats of the parent environment.
Normalization constant should ideally make the observation statistics approach those of a standard Gaussian distribution. This method computes a location and scale tensor that will empirically compute the mean and standard deviation of a Gaussian distribution fitted on data generated randomly with the parent environment for a given number of steps.
- Parameters:
num_iter (int) – number of random iterations to run in the environment.
reduce_dim (int or tuple of int, optional) – dimension to compute the mean and std over. Defaults to 0.
cat_dim (int, optional) – dimension along which the batches collected will be concatenated. It must be part equal to reduce_dim (if integer) or part of the reduce_dim tuple. Defaults to the same value as reduce_dim.
key (NestedKey, optional) – if provided, the summary statistics will be retrieved from that key in the resulting tensordicts. Otherwise, the first key in
ObservationNorm.in_keys
will be used.keep_dims (tuple of int, optional) – the dimensions to keep in the loc and scale. For instance, one may want the location and scale to have shape [C, 1, 1] when normalizing a 3D tensor over the last two dimensions, but not the third. Defaults to None.
- transform_input_spec(input_spec)[source]¶
Transforms the input spec such that the resulting spec matches transform mapping.
- Parameters:
input_spec (TensorSpec) – spec before the transform
- Returns:
expected spec after the transform
- transform_observation_spec(observation_spec: TensorSpec) TensorSpec [source]¶
Transforms the observation spec such that the resulting spec matches transform mapping.
- Parameters:
observation_spec (TensorSpec) – spec before the transform
- Returns:
expected spec after the transform