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| from typing import Any, List, Union, Optional | |
| import gym | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.utils import ENV_REGISTRY | |
| class MountainCarEnv(BaseEnv): | |
| """ | |
| Implementation of DI-engine's version of the Mountain Car deterministic MDP. | |
| Important references that contributed to the creation of this env: | |
| > Source code of OpenAI's mountain car gym : https://is.gd/y1FkMT | |
| > Gym documentation of mountain car : https://is.gd/29S0dt | |
| > Based off DI-engine existing implementation of cartpole_env.py | |
| > DI-engine's env creation conventions : https://is.gd/ZHLISj | |
| Only __init__ , step, seed and reset are mandatory & impt. | |
| The other methods are generally for convenience. | |
| """ | |
| def __init__(self, cfg: EasyDict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| self._replay_path = None | |
| # Following specifications from https://is.gd/29S0dt | |
| self._observation_space = gym.spaces.Box( | |
| low=np.array([-1.2, -0.07]), high=np.array([0.6, 0.07]), shape=(2, ), dtype=np.float32 | |
| ) | |
| self._action_space = gym.spaces.Discrete(3, start=0) | |
| self._reward_space = gym.spaces.Box(low=-1, high=0.0, shape=(1, ), dtype=np.float32) | |
| def seed(self, seed: int, dynamic_seed: bool = True) -> None: | |
| self._seed = seed | |
| self._dynamic_seed = dynamic_seed | |
| np.random.seed(self._seed) | |
| def reset(self) -> np.ndarray: | |
| # Instantiate environment if not already done so | |
| if not self._init_flag: | |
| self._env = gym.make('MountainCar-v0') | |
| self._init_flag = True | |
| # Check if we have a valid replay path and save replay video accordingly | |
| if self._replay_path is not None: | |
| 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)) | |
| ) | |
| # Set the seeds for randomization. | |
| 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) | |
| self._action_space.seed(self._seed + np_seed) | |
| elif hasattr(self, '_seed'): | |
| self._env.seed(self._seed) | |
| self._action_space.seed(self._seed) | |
| # Get first observation from original environment | |
| obs = self._env.reset() | |
| # Convert to numpy array as output | |
| obs = to_ndarray(obs).astype(np.float32) | |
| # Init final reward : cumulative sum of the real rewards obtained by a whole episode, | |
| # used to evaluate the agent Performance on this environment, not used for training. | |
| self._eval_episode_return = 0. | |
| return obs | |
| def step(self, action: np.ndarray) -> BaseEnvTimestep: | |
| # Making sure that input action is of numpy ndarray | |
| assert isinstance(action, np.ndarray), type(action) | |
| # Extract action as int, 0-dim array | |
| action = action.squeeze() | |
| # Take a step of faith into the unknown! | |
| obs, rew, done, info = self._env.step(action) | |
| # Cummulate reward | |
| self._eval_episode_return += rew | |
| # Save final cummulative reward when done. | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| # Making sure we conform to di-engine conventions | |
| obs = to_ndarray(obs) | |
| rew = to_ndarray([rew]).astype(np.float32) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def close(self) -> None: | |
| # If init flag is False, then reset() was never run, no point closing. | |
| if self._init_flag: | |
| self._env.close() | |
| self._init_flag = False | |
| 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: | |
| random_action = self.action_space.sample() | |
| random_action = to_ndarray([random_action], dtype=np.int64) | |
| return random_action | |
| 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 __repr__(self) -> str: | |
| return "DI-engine Mountain Car Env" | |