UnityMLAgentsWrapper

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

Unity ML-Agents environment wrapper.

GitHub: https://github.com/Unity-Technologies/ml-agents

Documentation: https://unity-technologies.github.io/ml-agents/Python-LLAPI/

Parameters:

env (mlagents_envs.environment.UnityEnvironment) – the ML-Agents environment to wrap.

Keyword Arguments:

• device (torch.device, optional) – if provided, the device on which the data is to be cast. Defaults to None.

• batch_size (torch.Size, optional) – the batch size of the environment. 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.

• group_map (MarlGroupMapType or Dict[str, List[str]]], optional) – how to group agents in tensordicts for input/output. See MarlGroupMapType for more info. If not specified, agents are grouped according to the group ID given by the Unity environment. Defaults to None.

• categorical_actions (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.

Variables:

available_envs – list of registered environments available to build

Examples

>>> from mlagents_envs.environment import UnityEnvironment
>>> base_env = UnityEnvironment()
>>> from torchrl.envs import UnityMLAgentsWrapper
>>> env = UnityMLAgentsWrapper(base_env)
>>> td = env.reset()
>>> td = env.step(td.update(env.full_action_spec.rand()))