ExcludeTransform

class torchrl.envs.transforms.ExcludeTransform(*excluded_keys, inverse: bool = False)

Excludes keys from the data.

Parameters:

• *excluded_keys (iterable of NestedKey) – The name of the keys to exclude. If the key is not present, it is simply ignored.

• inverse (bool, optional) – if True, the exclusion will occur during the inv call. Defaults to False.

Examples

>>> import gymnasium
>>> from torchrl.envs import GymWrapper
>>> env = TransformedEnv(
...     GymWrapper(gymnasium.make("Pendulum-v1")),
...     ExcludeTransform("truncated")
... )
>>> env.rollout(3)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([3]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=cpu,
    is_shared=False)

forward(tensordict: TensorDictBase) → TensorDictBase

Reads the input tensordict, and for the selected keys, applies the transform.

transform_output_spec(output_spec: CompositeSpec) → CompositeSpec

Transforms the output spec such that the resulting spec matches transform mapping.

This method should generally be left untouched. Changes should be implemented using transform_observation_spec(), transform_reward_spec() and transformfull_done_spec(). :param output_spec: spec before the transform :type output_spec: TensorSpec

Returns:

expected spec after the transform