BraxWrapper

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

Google Brax environment wrapper.

Brax offers a vectorized and differentiable simulation framework based on Jax. TorchRL’s wrapper incurs some overhead for the jax-to-torch conversion, but computational graphs can still be built on top of the simulated trajectories, allowing for backpropagation through the rollout.

GitHub: https://github.com/google/brax

Paper: https://arxiv.org/abs/2106.13281

Parameters:

• env (brax.envs.base.PipelineEnv) – the environment to wrap.

• categorical_action_encoding (bool, optional) – if True, categorical specs will be converted to the TorchRL equivalent (torchrl.data.Categorical), otherwise a one-hot encoding will be used (torchrl.data.OneHot). Defaults to False.

• cache_clear_frequency (int, optional) – automatically clear JAX’s internal cache every N steps to prevent memory leaks when using requires_grad=True. Defaults to False (deactivates automatic cache clearing).

Keyword Arguments:

• from_pixels (bool, optional) – Not yet supported.

• 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. In brax, this controls the number of environments simulated in parallel via JAX’s vmap on a single device (GPU/TPU). Brax leverages MuJoCo XLA (MJX) for hardware-accelerated batched simulation, enabling thousands of environments to run in parallel within a single process. 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 – environments available to build

Examples

>>> import brax.envs
>>> from torchrl.envs import BraxWrapper
>>> import torch
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> base_env = brax.envs.get_environment("ant")
>>> env = BraxWrapper(base_env, device=device)
>>> env.set_seed(0)
>>> td = env.reset()
>>> td["action"] = env.action_spec.rand()
>>> td = env.step(td)
>>> print(td)
TensorDict(
    fields={
        action: Tensor(torch.Size([8]), dtype=torch.float32),
        done: Tensor(torch.Size([1]), dtype=torch.bool),
        next: TensorDict(
            fields={
                observation: Tensor(torch.Size([87]), dtype=torch.float32)},
            batch_size=torch.Size([]),
            device=cpu,
            is_shared=False),
        observation: Tensor(torch.Size([87]), dtype=torch.float32),
        reward: Tensor(torch.Size([1]), dtype=torch.float32),
        state: TensorDict(...)},
    batch_size=torch.Size([]),
    device=cpu,
    is_shared=False)
>>> print(env.available_envs)
['acrobot', 'ant', 'fast', 'fetch', ...]

To take advante of Brax, one usually executes multiple environments at the same time. In the following example, we iteratively test different batch sizes and report the execution time for a short rollout:

Examples

>>> import torch
>>> from torch.utils.benchmark import Timer
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> for batch_size in [4, 16, 128]:
...     timer = Timer('''
... env.rollout(100)
... ''',
...     setup=f'''
... import brax.envs
... from torchrl.envs import BraxWrapper
... env = BraxWrapper(brax.envs.get_environment("ant"), batch_size=[{batch_size}], device="{device}")
... env.set_seed(0)
... env.rollout(2)
... ''')
...     print(batch_size, timer.timeit(10))
4
env.rollout(100)
setup: [...]
310.00 ms
1 measurement, 10 runs , 1 thread

16 env.rollout(100) setup: […] 268.46 ms 1 measurement, 10 runs , 1 thread

128 env.rollout(100) setup: […] 433.80 ms 1 measurement, 10 runs , 1 thread

One can backpropagate through the rollout and optimize the policy directly:

>>> import brax.envs
>>> from torchrl.envs import BraxWrapper
>>> from tensordict.nn import TensorDictModule
>>> from torch import nn
>>> import torch
>>>
>>> env = BraxWrapper(brax.envs.get_environment("ant"), batch_size=[10], requires_grad=True, cache_clear_frequency=100)
>>> env.set_seed(0)
>>> torch.manual_seed(0)
>>> policy = TensorDictModule(nn.Linear(27, 8), in_keys=["observation"], out_keys=["action"])
>>>
>>> td = env.rollout(10, policy)
>>>
>>> td["next", "reward"].mean().backward(retain_graph=True)
>>> print(policy.module.weight.grad.norm())
tensor(213.8605)