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| from typing import Any, Union, List, Optional | |
| import os | |
| import time | |
| import copy | |
| import numpy as np | |
| import gym | |
| from easydict import EasyDict | |
| from ding.envs import BaseEnv, BaseEnvTimestep, EvalEpisodeReturnWrapper | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.utils import ENV_REGISTRY | |
| import evogym.envs | |
| from evogym import WorldObject, sample_robot | |
| from evogym.sim import EvoSim | |
| class EvoGymEnv(BaseEnv): | |
| def default_config(cls: type) -> EasyDict: | |
| cfg = EasyDict(copy.deepcopy(cls.config)) | |
| cfg.cfg_type = cls.__name__ + 'Dict' | |
| return cfg | |
| config = dict( | |
| env_id='Walker-v0', | |
| robot='speed_bot', # refer to 'world data' for more robots configurations | |
| robot_h=5, # only used for random robots | |
| robot_w=5, # only used for random robots | |
| robot_pd=None, # only used for random robots, probability distributions of randomly generated components) | |
| robot_dir="" # only used for defined robots, path to the robot config, env/world_data/my_bot.json | |
| ) | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| self._replay_path = None | |
| if 'robot_dir' not in self._cfg.keys(): | |
| self._cfg = '../' | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| self._env = self._make_env() | |
| self._env.observation_space.dtype = np.float32 # To unify the format of envs in DI-engine | |
| self._observation_space = self._env.observation_space | |
| self.num_actuators = self._env.get_actuator_indices('robot').size | |
| # by default actions space is double (float64), create a new space with type of type float (float32) | |
| self._action_space = gym.spaces.Box(low=0.6, high=1.6, shape=(self.num_actuators, ), dtype=np.float32) | |
| self._reward_space = gym.spaces.Box( | |
| low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32 | |
| ) | |
| self._init_flag = True | |
| if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed: | |
| np_seed = 100 * np.random.randint(1, 1000) | |
| self._env.seed(self._seed + np_seed) | |
| elif hasattr(self, '_seed'): | |
| self._env.seed(self._seed) | |
| if self._replay_path is not None: | |
| gym.logger.set_level(gym.logger.DEBUG) | |
| # make render mode compatible with gym | |
| if gym.version.VERSION > '0.22.0': | |
| self._env.metadata.update({'render_modes': ["rgb_array"]}) | |
| else: | |
| self._env.metadata.update({'render.modes': ["rgb_array"]}) | |
| self._env = gym.wrappers.RecordVideo( | |
| self._env, | |
| video_folder=self._replay_path, | |
| episode_trigger=lambda episode_id: True, | |
| name_prefix='rl-video-{}-{}'.format(id(self), time.time()) | |
| ) | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs).astype('float32') | |
| return obs | |
| def close(self) -> None: | |
| if self._init_flag: | |
| self._env.close() | |
| self._init_flag = False | |
| def seed(self, seed: int, dynamic_seed: bool = True) -> None: | |
| self._seed = seed | |
| self._dynamic_seed = dynamic_seed | |
| np.random.seed(self._seed) | |
| def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep: | |
| action = to_ndarray(action).astype(np.float32) | |
| obs, rew, done, info = self._env.step(action) | |
| obs = to_ndarray(obs).astype(np.float32) | |
| rew = to_ndarray([rew]).astype(np.float32) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def _make_env(self): | |
| # robot configuration can be read from file or created randomly | |
| if self._cfg.robot in [None, 'random']: | |
| h, w = 5, 5 | |
| pd = None | |
| if 'robot_h' in self._cfg.keys(): | |
| assert self._cfg.robot_h > 0 | |
| h = self._cfg.robot_h | |
| if 'robot_w' in self._cfg.keys(): | |
| assert self._cfg.robot_w > 0 | |
| w = self._cfg.robot_w | |
| if 'robot_pd' in self._cfg.keys(): | |
| assert isinstance(self._cfg.robot_pd, np.ndarray) | |
| assert self._cfg.robot_w > 0 | |
| pd = self._cfg.robot_pd | |
| structure = sample_robot((h, w), pd) | |
| else: | |
| structure = self.read_robot_from_file(self._cfg.robot, self._cfg.robot_dir) | |
| env = gym.make(self._cfg.env_id, body=structure[0]) | |
| env = EvalEpisodeReturnWrapper(env) | |
| return env | |
| def enable_save_replay(self, replay_path: Optional[str] = None) -> None: | |
| if replay_path is None: | |
| replay_path = './video' | |
| self._replay_path = replay_path | |
| def random_action(self) -> np.ndarray: | |
| return self.action_space.sample() | |
| def __repr__(self) -> str: | |
| return "DI-engine EvoGym Env({})".format(self._cfg.env_id) | |
| def create_collector_env_cfg(cfg: dict) -> List[dict]: | |
| collector_cfg = copy.deepcopy(cfg) | |
| collector_env_num = collector_cfg.pop('collector_env_num', 1) | |
| return [collector_cfg for _ in range(collector_env_num)] | |
| def create_evaluator_env_cfg(cfg: dict) -> List[dict]: | |
| evaluator_cfg = copy.deepcopy(cfg) | |
| evaluator_env_num = evaluator_cfg.pop('evaluator_env_num', 1) | |
| return [evaluator_cfg for _ in range(evaluator_env_num)] | |
| def observation_space(self) -> gym.spaces.Space: | |
| return self._observation_space | |
| def action_space(self) -> gym.spaces.Space: | |
| return self._action_space | |
| def reward_space(self) -> gym.spaces.Space: | |
| return self._reward_space | |
| def read_robot_from_file(file_name, root_dir='../'): | |
| possible_paths = [ | |
| os.path.join(file_name), | |
| os.path.join(f'{file_name}.npz'), | |
| os.path.join(f'{file_name}.json'), | |
| os.path.join(root_dir, 'world_data', file_name), | |
| os.path.join(root_dir, 'world_data', f'{file_name}.npz'), | |
| os.path.join(root_dir, 'world_data', f'{file_name}.json'), | |
| ] | |
| best_path = None | |
| for path in possible_paths: | |
| if os.path.exists(path): | |
| best_path = path | |
| break | |
| if best_path.endswith('json'): | |
| robot_object = WorldObject.from_json(best_path) | |
| return (robot_object.get_structure(), robot_object.get_connections()) | |
| if best_path.endswith('npz'): | |
| structure_data = np.load(best_path) | |
| structure = [] | |
| for key, value in structure_data.items(): | |
| structure.append(value) | |
| return tuple(structure) | |
| return None | |