reference/generated/torchrl.envs.DMControlEnv

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DMControlEnv

torchrl.envs.DMControlEnv(*args, num_workers: int | None = None, **kwargs)

DeepMind Control lab environment wrapper.

The DeepMind control library can be found here: deepmind/dm_control.

Paper: https://arxiv.org/abs/2006.12983

Parameters:

• env_name (str) – name of the environment.

• task_name (str) – name of the task.

• num_workers (int, optional) – number of parallel environments. Defaults to 1. When num_workers > 1, a ParallelEnv is returned instead of a single environment. Its workers provide their metadata directly, avoiding a temporary environment construction in the parent process.

Keyword Arguments:

• from_pixels (bool, optional) – if True, an attempt to return the pixel observations from the env will be performed. By default, these observations will be written under the "pixels" entry. Defaults to False.

• pixels_only (bool, optional) – if True, only the pixel observations will be returned (by default under the "pixels" entry in the output tensordict). If False, observations (eg, states) and pixels will be returned whenever from_pixels=True. Defaults to True.

• frame_skip (int, optional) – if provided, indicates for how many steps the same action is to be repeated. The observation returned will be the last observation of the sequence, whereas the reward will be the sum of rewards across steps.

• device (torch.device, optional) – if provided, the device on which the data is to be cast. Defaults to torch.device("cpu").

• batch_size (torch.Size, optional) – the batch size of the environment. Should match the leading dimensions of all observations, done states, rewards, actions and infos. Defaults to torch.Size([]).

• allow_done_after_reset (bool, optional) – if True, it is tolerated for envs to be done just after reset() is called. Defaults to False.

Variables:

available_envs (list) – a list of Tuple[str, List[str]] representing the environment / task pairs available.

Examples

>>> from torchrl.envs import DMControlEnv
>>> env = DMControlEnv(env_name="cheetah", task_name="run",
...    from_pixels=True, frame_skip=4)
>>> td = env.rand_step()
>>> print(td)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([6]), device=cpu, dtype=torch.float64, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                pixels: Tensor(shape=torch.Size([240, 320, 3]), device=cpu, dtype=torch.uint8, is_shared=False),
                position: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float64, is_shared=False),
                reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float64, 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),
                velocity: Tensor(shape=torch.Size([9]), device=cpu, dtype=torch.float64, is_shared=False)},
            batch_size=torch.Size([]),
            device=cpu,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=cpu,
    is_shared=False)
>>> print(env.available_envs)
[('acrobot', ['swingup', 'swingup_sparse']), ...]
>>> # Run multiple envs in parallel without parent-side construction
>>> env = DMControlEnv("cheetah", "run", num_workers=4)
>>> env.reset()