RenameTransform

class torchrl.envs.transforms.RenameTransform(in_keys, out_keys, in_keys_inv=None, out_keys_inv=None, create_copy=False)

A transform to rename entries in the output tensordict (or input tensordict via the inverse keys).

Parameters:

• in_keys (sequence of NestedKey) – the entries to rename.

• out_keys (sequence of NestedKey) – the name of the entries after renaming.

• in_keys_inv (sequence of NestedKey, optional) – the entries to rename in the input tensordict, which will be passed to EnvBase._step().

• out_keys_inv (sequence of NestedKey, optional) – the names of the entries in the input tensordict after renaming.

• create_copy (bool, optional) – if True, the entries will be copied with a different name rather than being renamed. This allows for renaming immutable entries such as "reward" and "done".

Examples

>>> from torchrl.envs.libs.gym import GymEnv
>>> env = TransformedEnv(
...     GymEnv("Pendulum-v1"),
...     RenameTransform(["observation", ], ["stuff",], create_copy=False),
... )
>>> tensordict = env.rollout(3)
>>> print(tensordict)
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),
                reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                stuff: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([3]),
            device=cpu,
            is_shared=False),
        stuff: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=cpu,
    is_shared=False)
>>> # if the output is also an input, we need to rename if both ways:
>>> from torchrl.envs.libs.brax import BraxEnv
>>> env = TransformedEnv(
...     BraxEnv("fast"),
...     RenameTransform(["state"], ["newname"], ["state"], ["newname"])
... )
>>> _ = env.set_seed(1)
>>> tensordict = env.rollout(3)
>>> assert "newname" in tensordict.keys()
>>> assert "state" not in tensordict.keys()

forward(tensordict: TensorDictBase) → TensorDictBase

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

transform_input_spec(input_spec: Composite) → Composite

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

Returns:

expected spec after the transform