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| from typing import Any, Union, Optional | |
| import gym | |
| import torch | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.utils import ENV_REGISTRY | |
| from ding.torch_utils import to_ndarray, to_list | |
| class PendulumEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._act_scale = cfg.act_scale | |
| self._env = gym.make('Pendulum-v1') | |
| self._init_flag = False | |
| self._replay_path = None | |
| if 'continuous' in cfg.keys(): | |
| self._continuous = cfg.continuous | |
| else: | |
| self._continuous = True | |
| self._observation_space = gym.spaces.Box( | |
| low=np.array([-1.0, -1.0, -8.0]), high=np.array([1.0, 1.0, 8.0]), shape=(3, ), dtype=np.float32 | |
| ) | |
| if self._continuous: | |
| self._action_space = gym.spaces.Box(low=-2.0, high=2.0, shape=(1, ), dtype=np.float32) | |
| else: | |
| self._discrete_action_num = 11 | |
| self._action_space = gym.spaces.Discrete(self._discrete_action_num) | |
| self._action_space.seed(0) # default seed | |
| self._reward_space = gym.spaces.Box( | |
| low=-1 * (3.14 * 3.14 + 0.1 * 8 * 8 + 0.001 * 2 * 2), high=0.0, shape=(1, ), dtype=np.float32 | |
| ) | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| self._env = gym.make('Pendulum-v1') | |
| 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)) | |
| ) | |
| 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) | |
| self._action_space.seed(self._seed + np_seed) | |
| elif hasattr(self, '_seed'): | |
| self._env.seed(self._seed) | |
| self._action_space.seed(self._seed) | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs).astype(np.float32) | |
| self._eval_episode_return = 0. | |
| 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: np.ndarray) -> BaseEnvTimestep: | |
| assert isinstance(action, np.ndarray), type(action) | |
| # if require discrete env, convert actions to [-1 ~ 1] float actions | |
| if not self._continuous: | |
| action = (action / (self._discrete_action_num - 1)) * 2 - 1 | |
| # scale into [-2, 2] | |
| if self._act_scale: | |
| action = affine_transform(action, min_val=self._env.action_space.low, max_val=self._env.action_space.high) | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| obs = to_ndarray(obs).astype(np.float32) | |
| # wrapped to be transfered to a array with shape (1,) | |
| rew = to_ndarray([rew]).astype(np.float32) | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| 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: | |
| # consider discrete | |
| if self._continuous: | |
| random_action = self.action_space.sample().astype(np.float32) | |
| else: | |
| 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 Pendulum Env({})".format(self._cfg.env_id) | |
| class MBPendulumEnv(PendulumEnv): | |
| def termination_fn(self, next_obs: torch.Tensor) -> torch.Tensor: | |
| """ | |
| Overview: | |
| This function determines whether each state is a terminated state | |
| .. note:: | |
| Done is always false for pendulum, according to\ | |
| <https://github.com/openai/gym/blob/master/gym/envs/classic_control/pendulum.py>. | |
| """ | |
| done = torch.zeros_like(next_obs.sum(-1)).bool() | |
| return done | |