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| #!/usr/bin/env python | |
| # Copyright 2024 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import importlib | |
| import gymnasium as gym | |
| from lerobot.common.envs.configs import AlohaEnv, EnvConfig, PushtEnv, XarmEnv | |
| def make_env_config(env_type: str, **kwargs) -> EnvConfig: | |
| if env_type == "aloha": | |
| return AlohaEnv(**kwargs) | |
| elif env_type == "pusht": | |
| return PushtEnv(**kwargs) | |
| elif env_type == "xarm": | |
| return XarmEnv(**kwargs) | |
| else: | |
| raise ValueError(f"Policy type '{env_type}' is not available.") | |
| def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> gym.vector.VectorEnv | None: | |
| """Makes a gym vector environment according to the config. | |
| Args: | |
| cfg (EnvConfig): the config of the environment to instantiate. | |
| n_envs (int, optional): The number of parallelized env to return. Defaults to 1. | |
| use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to | |
| False. | |
| Raises: | |
| ValueError: if n_envs < 1 | |
| ModuleNotFoundError: If the requested env package is not installed | |
| Returns: | |
| gym.vector.VectorEnv: The parallelized gym.env instance. | |
| """ | |
| if n_envs < 1: | |
| raise ValueError("`n_envs must be at least 1") | |
| package_name = f"gym_{cfg.type}" | |
| try: | |
| importlib.import_module(package_name) | |
| except ModuleNotFoundError as e: | |
| print(f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.type}]'`") | |
| raise e | |
| gym_handle = f"{package_name}/{cfg.task}" | |
| # batched version of the env that returns an observation of shape (b, c) | |
| env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv | |
| env = env_cls( | |
| [lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs) for _ in range(n_envs)] | |
| ) | |
| return env | |