ProcgenWrapper

torchrl.envs.ProcgenWrapper(*args, **kwargs)

OpenAI Procgen environment wrapper.

Wraps an existing procgen.ProcgenEnv instance and exposes it under the TorchRL environment API.

This wrapper is responsible for: - Converting Procgen observations ({"rgb": np.ndarray}) to Torch tensors - Handling vectorized Procgen semantics - Producing TorchRL-compliant TensorDict outputs

Parameters:

env (procgen.ProcgenEnv) – an already constructed Procgen environment.

Keyword Arguments:

• device (torch.device | str, optional) – device on which tensors are placed.

• batch_size (torch.Size, optional) – expected batch size.

• allow_done_after_reset (bool, optional) – tolerate done right after reset.

Variables:

available_envs (List[str]) – list of Procgen environment ids.

Examples

>>> import procgen
>>> from torchrl.envs.libs.procgen import ProcgenWrapper
>>> env = procgen.ProcgenEnv(4, "coinrun")
>>> env = ProcgenWrapper(env=env)
>>> td = env.reset()
>>> print(td)
TensorDict(
    fields={
        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        observation: Tensor(shape=torch.Size([4, 3, 64, 64]), device=cpu, dtype=torch.uint8, is_shared=False),
        reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False
)
>>> print(td["observation"].shape)
torch.Size([4, 3, 64, 64])