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| import copy | |
| import os | |
| from typing import Dict, Optional | |
| import gym | |
| import gym_hybrid | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from easydict import EasyDict | |
| from matplotlib import animation | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.envs.common import affine_transform | |
| from ding.torch_utils import to_ndarray | |
| from ding.utils import ENV_REGISTRY | |
| class GymHybridEnv(BaseEnv): | |
| default_env_id = ['Sliding-v0', 'Moving-v0', 'HardMove-v0'] | |
| def default_config(cls: type) -> EasyDict: | |
| cfg = EasyDict(copy.deepcopy(cls.config)) | |
| cfg.cfg_type = cls.__name__ + 'Dict' | |
| return cfg | |
| config = dict( | |
| env_id='Moving-v0', | |
| act_scale=True, | |
| ) | |
| def __init__(self, cfg: EasyDict) -> None: | |
| self._cfg = cfg | |
| self._env_id = cfg.env_id | |
| assert self._env_id in self.default_env_id | |
| self._act_scale = cfg.act_scale | |
| self._replay_path = None | |
| self._save_replay = False | |
| self._save_replay_count = 0 | |
| self._init_flag = False | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| if self._env_id == 'HardMove-v0': | |
| self._env = gym.make(self._env_id, num_actuators=self._cfg.num_actuators) | |
| else: | |
| self._env = gym.make(self._env_id) | |
| self._observation_space = self._env.observation_space | |
| self._action_space = self._env.action_space | |
| 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) | |
| self._eval_episode_return = 0 | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs).astype(np.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: Dict) -> BaseEnvTimestep: | |
| if self._act_scale: | |
| if self._env_id == 'HardMove-v0': | |
| action = [ | |
| action['action_type'], [affine_transform(i, min_val=-1, max_val=1) for i in action['action_args']] | |
| ] | |
| else: | |
| # acceleration_value. | |
| action['action_args'][0] = affine_transform(action['action_args'][0], min_val=0, max_val=1) | |
| # rotation_value. Following line can be omitted, because in the affine_transform function, | |
| # we have already done the clip(-1,1) operation | |
| action['action_args'][1] = affine_transform(action['action_args'][1], min_val=-1, max_val=1) | |
| action = [action['action_type'], action['action_args']] | |
| if self._save_replay: | |
| self._frames.append(self._env.render(mode='rgb_array')) | |
| obs, rew, done, info = self._env.step(action) | |
| obs = to_ndarray(obs) | |
| if isinstance(obs, list): # corner case | |
| for i in range(len(obs)): | |
| if len(obs[i].shape) == 0: | |
| obs[i] = np.array([obs[i]]) | |
| obs = np.concatenate(obs) | |
| assert isinstance(obs, np.ndarray) and obs.shape == (10, ) | |
| obs = obs.astype(np.float32) | |
| rew = to_ndarray([rew]) # wrapped to be transferred to a numpy array with shape (1,) | |
| if isinstance(rew, list): | |
| rew = rew[0] | |
| assert isinstance(rew, np.ndarray) and rew.shape == (1, ) | |
| self._eval_episode_return += rew.item() | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| if self._save_replay: | |
| if self._env_id == 'HardMove-v0': | |
| self._env_id = f'hardmove_n{self._cfg.num_actuators}' | |
| path = os.path.join( | |
| self._replay_path, '{}_episode_{}.gif'.format(self._env_id, self._save_replay_count) | |
| ) | |
| self.display_frames_as_gif(self._frames, path) | |
| self._frames = [] | |
| self._save_replay_count += 1 | |
| info['action_args_mask'] = np.array([[1, 0], [0, 1], [0, 0]]) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def random_action(self) -> Dict: | |
| # action_type: 0, 1, 2 | |
| # action_args: | |
| # - acceleration_value: [0, 1] | |
| # - rotation_value: [-1, 1] | |
| raw_action = self._action_space.sample() | |
| return {'action_type': raw_action[0], 'action_args': raw_action[1]} | |
| def __repr__(self) -> str: | |
| return "DI-engine gym hybrid Env" | |
| 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 enable_save_replay(self, replay_path: Optional[str] = None) -> None: | |
| if replay_path is None: | |
| replay_path = './video' | |
| self._replay_path = replay_path | |
| self._save_replay = True | |
| self._save_replay_count = 0 | |
| self._frames = [] | |
| def display_frames_as_gif(frames: list, path: str) -> None: | |
| patch = plt.imshow(frames[0]) | |
| plt.axis('off') | |
| def animate(i): | |
| patch.set_data(frames[i]) | |
| anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames), interval=5) | |
| anim.save(path, writer='imagemagick', fps=20) | |