UnityMLAgentsEnv¶
- torchrl.envs.UnityMLAgentsEnv(*args, **kwargs)[source]¶
Unity ML-Agents environment wrapper.
GitHub: https://github.com/Unity-Technologies/ml-agents
Documentation: https://unity-technologies.github.io/ml-agents/Python-LLAPI/
This class can be provided any of the optional initialization arguments that
mlagents_envs.environment.UnityEnvironmentclass provides. For a list of these arguments, see: https://unity-technologies.github.io/ml-agents/Python-LLAPI-Documentation/#__init__If both
file_nameandregistered_nameare given, an error is raised.If neither
file_namenor``registered_name`` are given, the environment setup waits on a localhost port, and the user must execute a Unity ML-Agents environment binary for to connect to it.- Parameters:
file_name (str, optional) – if provided, the path to the Unity environment binary. Defaults to
None.registered_name (str, optional) – if provided, the Unity environment binary is loaded from the default ML-Agents registry. The list of registered environments is in
available_envs. Defaults toNone.
- 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 bedonejust afterreset()is called. Defaults toFalse.group_map (MarlGroupMapType or Dict[str, List[str]]], optional) – how to group agents in tensordicts for input/output. See
MarlGroupMapTypefor more info. If not specified, agents are grouped according to the group ID given by the Unity environment. Defaults toNone.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 toFalse.
- Variables:
available_envs – list of registered environments available to build
Examples
>>> from torchrl.envs import UnityMLAgentsEnv >>> env = UnityMLAgentsEnv(registered_name='3DBall') >>> td = env.reset() >>> td = env.step(td.update(env.full_action_spec.rand())) >>> td TensorDict( fields={ group_0: TensorDict( fields={ agent_0: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_10: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_11: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_1: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_2: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_3: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_4: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_5: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_6: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_7: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_8: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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), agent_9: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), continuous_action: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, 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)}, batch_size=torch.Size([]), device=None, is_shared=False), next: TensorDict( fields={ group_0: TensorDict( fields={ agent_0: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_10: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_11: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_1: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_2: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_3: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_4: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_5: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_6: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_7: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_8: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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), agent_9: TensorDict( fields={ VectorSensor_size8: Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), group_reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, 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)}, batch_size=torch.Size([]), device=None, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False)
