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| import copy | |
| from datetime import datetime | |
| from typing import Union, Dict | |
| import gymnasium as gym | |
| import numpy as np | |
| from ding.envs import BaseEnvTimestep | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.torch_utils import to_ndarray | |
| from ding.utils import ENV_REGISTRY | |
| from easydict import EasyDict | |
| from zoo.classic_control.cartpole.envs.cartpole_lightzero_env import CartPoleEnv | |
| class PendulumEnv(CartPoleEnv): | |
| """ | |
| LightZero version of the classic Pendulum environment. This class includes methods for resetting, closing, and | |
| stepping through the environment, as well as seeding for reproducibility, saving replay videos, and generating random | |
| actions. It also includes properties for accessing the observation space, action space, and reward space of the | |
| environment. | |
| """ | |
| def default_config(cls: type) -> EasyDict: | |
| cfg = EasyDict(copy.deepcopy(cls.config)) | |
| cfg.cfg_type = cls.__name__ + 'Dict' | |
| return cfg | |
| config = dict( | |
| # (bool) Whether to use continuous action space | |
| continuous=True, | |
| # replay_path (str or None): The path to save the replay video. If None, the replay will not be saved. | |
| # Only effective when env_manager.type is 'base'. | |
| replay_path=None, | |
| # (bool) Whether to scale action into [-2, 2] | |
| act_scale=True, | |
| ) | |
| def __init__(self, cfg: dict) -> None: | |
| """ | |
| Initialize the environment with a configuration dictionary. Sets up spaces for observations, actions, and rewards. | |
| """ | |
| self._cfg = cfg | |
| self._act_scale = cfg.act_scale | |
| try: | |
| self._env = gym.make('Pendulum-v1', render_mode="rgb_array") | |
| except: | |
| self._env = gym.make('Pendulum-v0', render_mode="rgb_array") | |
| self._init_flag = False | |
| self._replay_path = cfg.replay_path | |
| self._continuous = cfg.get("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) -> Dict[str, np.ndarray]: | |
| """ | |
| Reset the environment. If it hasn't been initialized yet, this method also handles that. It also handles seeding | |
| if necessary. Returns the first observation. | |
| """ | |
| if not self._init_flag: | |
| try: | |
| self._env = gym.make('Pendulum-v1', render_mode="rgb_array") | |
| except: | |
| self._env = gym.make('Pendulum-v0', render_mode="rgb_array") | |
| if self._replay_path is not None: | |
| timestamp = datetime.now().strftime("%Y%m%d%H%M%S") | |
| video_name = f'{self._env.spec.id}-video-{timestamp}' | |
| self._env = gym.wrappers.RecordVideo( | |
| self._env, | |
| video_folder=self._replay_path, | |
| episode_trigger=lambda episode_id: True, | |
| name_prefix=video_name | |
| ) | |
| 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._seed = self._seed + np_seed | |
| self._action_space.seed(self._seed) | |
| obs, _ = self._env.reset(seed=self._seed) | |
| elif hasattr(self, '_seed'): | |
| self._action_space.seed(self._seed) | |
| obs, _ = self._env.reset(seed=self._seed) | |
| else: | |
| obs, _ = self._env.reset() | |
| obs = to_ndarray(obs).astype(np.float32) | |
| self._eval_episode_return = 0. | |
| if not self._continuous: | |
| action_mask = np.ones(self.discrete_action_num, 'int8') | |
| else: | |
| action_mask = None | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| return obs | |
| def step(self, action: Union[int, np.ndarray]) -> BaseEnvTimestep: | |
| """ | |
| Overview: | |
| Step the environment forward with the provided action. This method returns the next state of the environment | |
| (observation, reward, done flag, and info dictionary) encapsulated in a BaseEnvTimestep object. | |
| Arguments: | |
| - action (:obj:`Union[int, np.ndarray]`): The action to be performed in the environment. | |
| Returns: | |
| - timestep (:obj:`BaseEnvTimestep`): An object containing the new observation, reward, done flag, | |
| and info dictionary. | |
| .. note:: | |
| - If the environment requires discrete actions, they are converted to float actions in the range [-1, 1]. | |
| - If action scaling is enabled, continuous actions are scaled into the range [-2, 2]. | |
| - For each step, the cumulative reward (`_eval_episode_return`) is updated. | |
| - If the episode ends (done is True), the total reward for the episode is stored in the info dictionary | |
| under the key 'eval_episode_return'. | |
| - If the environment requires discrete actions, an action mask is created, otherwise, it's None. | |
| - Observations are returned in a dictionary format containing 'observation', 'action_mask', and 'to_play'. | |
| """ | |
| if isinstance(action, int): | |
| action = np.array(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 the continous action 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, terminated, truncated, info = self._env.step(action) | |
| done = terminated or truncated | |
| self._eval_episode_return += rew | |
| obs = to_ndarray(obs).astype(np.float32) | |
| # wrapped to be transferred to an array with shape (1,) | |
| rew = to_ndarray([rew]).astype(np.float32) | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| if not self._continuous: | |
| action_mask = np.ones(self.discrete_action_num, 'int8') | |
| else: | |
| action_mask = None | |
| obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1} | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def random_action(self) -> np.ndarray: | |
| """ | |
| Generate a random action using the action space's sample method. Returns a numpy array containing the action. | |
| """ | |
| 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 __repr__(self) -> str: | |
| """ | |
| String representation of the environment. | |
| """ | |
| return "LightZero Pendulum Env({})".format(self._cfg.env_id) | |