Reward2GoTransform

class torchrl.envs.transforms.Reward2GoTransform(gamma: Optional[Union[float, torch.Tensor]] = 1.0, in_keys: Sequence[NestedKey] | None = None, out_keys: Sequence[NestedKey] | None = None, done_key: Optional[NestedKey] = 'done')

Calculates the reward to go based on the episode reward and a discount factor.

As the Reward2GoTransform is only an inverse transform the in_keys will be directly used for the in_keys_inv. The reward-to-go can be only calculated once the episode is finished. Therefore, the transform should be applied to the replay buffer and not to the collector or within an environment.

Parameters:

• gamma (float or torch.Tensor) – the discount factor. Defaults to 1.0.

• in_keys (sequence of NestedKey) – the entries to rename. Defaults to ("next", "reward") if none is provided.

• out_keys (sequence of NestedKey) – the entries to rename. Defaults to the values of in_keys if none is provided.

• done_key (NestedKey) – the done entry. Defaults to "done".

• truncated_key (NestedKey) – the truncated entry. Defaults to "truncated". If no truncated entry is found, only the "done" will be used.

Examples

>>> # Using this transform as part of a replay buffer
>>> from torchrl.data import ReplayBuffer, LazyTensorStorage
>>> torch.manual_seed(0)
>>> r2g = Reward2GoTransform(gamma=0.99, out_keys=["reward_to_go"])
>>> rb = ReplayBuffer(storage=LazyTensorStorage(100), transform=r2g)
>>> batch, timesteps = 4, 5
>>> done = torch.zeros(batch, timesteps, 1, dtype=torch.bool)
>>> for i in range(batch):
...     while not done[i].any():
...         done[i] = done[i].bernoulli_(0.1)
>>> reward = torch.ones(batch, timesteps, 1)
>>> td = TensorDict(
...     {"next": {"done": done, "reward": reward}},
...     [batch, timesteps],
... )
>>> rb.extend(td)
>>> sample = rb.sample(1)
>>> print(sample["next", "reward"])
tensor([[[1.],
         [1.],
         [1.],
         [1.],
         [1.]]])
>>> print(sample["reward_to_go"])
tensor([[[4.9010],
         [3.9404],
         [2.9701],
         [1.9900],
         [1.0000]]])

One can also use this transform directly with a collector: make sure to append the inv method of the transform.

Examples

>>> from torchrl.envs.utils import RandomPolicy        >>> from torchrl.collectors import SyncDataCollector
>>> from torchrl.envs.libs.gym import GymEnv
>>> t = Reward2GoTransform(gamma=0.99, out_keys=["reward_to_go"])
>>> env = GymEnv("Pendulum-v1")
>>> collector = SyncDataCollector(
...     env,
...     RandomPolicy(env.action_spec),
...     frames_per_batch=200,
...     total_frames=-1,
...     postproc=t.inv
... )
>>> for data in collector:
...     break
>>> print(data)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        collector: TensorDict(
            fields={
                traj_ids: Tensor(shape=torch.Size([200]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([200]),
            device=cpu,
            is_shared=False),
        done: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([200, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([200]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([200, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        reward: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        reward_to_go: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([200]),
    device=cpu,
    is_shared=False)

Using this transform as part of an env will raise an exception

Examples

>>> t = Reward2GoTransform(gamma=0.99)
>>> TransformedEnv(GymEnv("Pendulum-v1"), t)  # crashes

Note

In settings where multiple done entries are present, one should build a single Reward2GoTransform for each done-reward pair.

forward(tensordict: TensorDictBase) → TensorDictBase

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