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import copy
import os
from datetime import datetime
from typing import List, Dict
import gymnasium as gym
import numpy as np
from ding.envs import BaseEnvTimestep
from ding.envs.common 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
@ENV_REGISTRY.register('bipedalwalker')
class BipedalWalkerEnv(CartPoleEnv):
"""
Overview:
The BipedalWalker Environment class for LightZero algo.. This class is a wrapper of the gym BipedalWalker environment, with additional
functionalities like replay saving and seed setting. The class is registered in ENV_REGISTRY with the key 'bipedalwalker'.
"""
config = dict(
# (str) The gym environment name.
env_name="BipedalWalker-v3",
# (str) The type of the environment. Options: {'normal', 'hardcore'}
env_type='normal',
# (bool) If True, save the replay as a gif file.
save_replay_gif=False,
# (str or None) The path to save the replay gif. If None, the replay gif will not be saved.
replay_path_gif=None,
# 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) If True, the action will be scaled.
act_scale=True,
# (bool) If True, the reward will be clipped to [-10, +inf].
rew_clip=True,
# (int) The maximum number of steps for each episode during collection.
collect_max_episode_steps=int(1.08e5),
# (int) The maximum number of steps for each episode during evaluation.
eval_max_episode_steps=int(1.08e5),
)
@classmethod
def default_config(cls: type) -> EasyDict:
"""
Overview:
Return the default configuration of the class.
Returns:
- cfg (:obj:`EasyDict`): Default configuration dict.
"""
cfg = EasyDict(copy.deepcopy(cls.config))
cfg.cfg_type = cls.__name__ + 'Dict'
return cfg
def __init__(self, cfg: dict) -> None:
"""
Overview:
Initialize the BipedalWalker environment.
Arguments:
- cfg (:obj:`dict`): Configuration dict. The dict should include keys like 'env_name', 'replay_path', etc.
"""
self._cfg = cfg
self._init_flag = False
self._env_name = cfg.env_name
self._act_scale = cfg.act_scale
self._rew_clip = cfg.rew_clip
self._replay_path = cfg.replay_path
self._replay_path_gif = cfg.replay_path_gif
self._save_replay_gif = cfg.save_replay_gif
self._save_replay_count = 0
def reset(self) -> Dict[str, np.ndarray]:
"""
Overview:
Reset the environment and return the initial observation.
Returns:
- obs (:obj:`np.ndarray`): The initial observation after resetting.
"""
if not self._init_flag:
assert self._cfg.env_type in ['normal', 'hardcore'], "env_type must be in ['normal', 'hardcore']"
if self._cfg.env_type == 'normal':
self._env = gym.make('BipedalWalker-v3', render_mode="rgb_array")
elif self._cfg.env_type == 'hardcore':
self._env = gym.make('BipedalWalker-v3', hardcore=True, render_mode="rgb_array")
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 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
)
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
obs, _ = self._env.reset(seed=self._seed) # using the reset method of Gymnasium env
elif hasattr(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 self._save_replay_gif:
self._frames = []
action_mask = None
obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1}
return obs
def step(self, action: np.ndarray) -> BaseEnvTimestep:
"""
Overview:
Take a step in the environment with the given action.
Arguments:
- action (:obj:`np.ndarray`): The action to be taken.
Returns:
- timestep (:obj:`BaseEnvTimestep`): The timestep information including observation, reward, done flag, and info.
"""
assert isinstance(action, np.ndarray), type(action)
if action.shape == (1,):
action = action.squeeze() # 0-dim array
if self._act_scale:
action = affine_transform(action, min_val=self.action_space.low, max_val=self.action_space.high)
if self._save_replay_gif:
self._frames.append(self._env.render())
obs, rew, terminated, truncated, info = self._env.step(action)
done = terminated or truncated
action_mask = None
obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1}
self._eval_episode_return += rew
if self._rew_clip:
rew = max(-10, rew)
rew = np.float32(rew)
if done:
info['eval_episode_return'] = self._eval_episode_return
if self._save_replay_gif:
if not os.path.exists(self._replay_path_gif):
os.makedirs(self._replay_path_gif)
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
path = os.path.join(
self._replay_path_gif,
'{}_episode_{}_seed{}_{}.gif'.format(self._env_name, self._save_replay_count, self._seed, timestamp)
)
self.display_frames_as_gif(self._frames, path)
print(f'save episode {self._save_replay_count} in {self._replay_path_gif}!')
self._save_replay_count += 1
obs = to_ndarray(obs)
rew = to_ndarray([rew]) # wrapped to be transferred to a array with shape (1,)
return BaseEnvTimestep(obs, rew, done, info)
@property
def legal_actions(self) -> np.ndarray:
"""
Overview:
Get the legal actions in the environment.
Returns:
- legal_actions (:obj:`np.ndarray`): An array of legal actions.
"""
return np.arange(self._action_space.n)
@staticmethod
def display_frames_as_gif(frames: list, path: str) -> None:
import imageio
imageio.mimsave(path, frames, fps=20)
def random_action(self) -> np.ndarray:
random_action = self.action_space.sample()
if isinstance(random_action, np.ndarray):
pass
elif isinstance(random_action, int):
random_action = to_ndarray([random_action], dtype=np.int64)
return random_action
def __repr__(self) -> str:
return "LightZero BipedalWalker Env"
@staticmethod
def create_collector_env_cfg(cfg: dict) -> List[dict]:
"""
Overview:
Create a list of environment configurations for the collector.
Arguments:
- cfg (:obj:`dict`): The base configuration dict.
Returns:
- cfgs (:obj:`List[dict]`): The list of environment configurations.
"""
collector_env_num = cfg.pop('collector_env_num')
cfg = copy.deepcopy(cfg)
cfg.max_episode_steps = cfg.collect_max_episode_steps
return [cfg for _ in range(collector_env_num)]
@staticmethod
def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
"""
Overview:
Create a list of environment configurations for the evaluator.
Arguments:
- cfg (:obj:`dict`): The base configuration dict.
Returns:
- cfgs (:obj:`List[dict]`): The list of environment configurations.
"""
evaluator_env_num = cfg.pop('evaluator_env_num')
cfg = copy.deepcopy(cfg)
cfg.max_episode_steps = cfg.eval_max_episode_steps
return [cfg for _ in range(evaluator_env_num)]
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