SelectTransform

class torchrl.envs.transforms.SelectTransform(*selected_keys: NestedKey, keep_rewards: bool = True, keep_dones: bool = True)

Select keys from the input tensordict.

In general, the ExcludeTransform should be preferred: this transforms also

selects the “action” (or other keys from input_spec), “done” and “reward” keys but other may be necessary.

Parameters:

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

Keyword Arguments:

• keep_rewards (bool, optional) – if False, the reward keys must be provided if they should be kept. Defaults to True.

• keep_dones (bool, optional) – if False, the done keys must be provided if they should be kept. Defaults to True.

Examples

>>> import gymnasium
>>> from torchrl.envs import GymWrapper
>>> env = TransformedEnv(
...     GymWrapper(gymnasium.make("Pendulum-v1")),
...     SelectTransform("observation", "reward", "done", keep_dones=False), # we leave done behind
... )
>>> env.rollout(3)  # the truncated key is now absent
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(next_tensordict: TensorDictBase) → TensorDictBase

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

By default, this method:

• calls directly _apply_transform().

• does not call _step() or _call().

This method is not called within env.step at any point. However, is is called within sample().

Note

forward also works with regular keyword arguments using dispatch to cast the args names to the keys.

Examples

>>> class TransformThatMeasuresBytes(Transform):
...     '''Measures the number of bytes in the tensordict, and writes it under `"bytes"`.'''
...     def __init__(self):
...         super().__init__(in_keys=[], out_keys=["bytes"])
...
...     def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
...         bytes_in_td = tensordict.bytes()
...         tensordict["bytes"] = bytes
...         return tensordict
>>> t = TransformThatMeasuresBytes()
>>> env = env.append_transform(t) # works within envs
>>> t(TensorDict(a=0))  # Works offline too.

transform_output_spec(output_spec: Composite) → Composite

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 transform_full_done_spec(). :param output_spec: spec before the transform :type output_spec: TensorSpec

Returns:

expected spec after the transform