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import gym
import copy
import numpy as np
from typing import List
from easydict import EasyDict
from ding.utils import ENV_REGISTRY
from ding.torch_utils import to_ndarray
from ding.envs import BaseEnv, BaseEnvTimestep
from .sokoban_wrappers import wrap_sokoban
@ENV_REGISTRY.register('sokoban')
class SokobanEnv(BaseEnv):
def __init__(self, cfg: dict) -> None:
self._cfg = cfg
self._env_id = cfg.env_id
self._init_flag = False
self._save_replay = 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 reset(self) -> np.ndarray:
if not self._init_flag:
self._env = self._make_env(only_info=False)
self._init_flag = True
if self._save_replay:
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._env.observation_space.dtype = np.float32 # To unify the format of envs in DI-engine
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
)
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)
obs = self._env.reset()
obs = to_ndarray(obs).astype('float32')
self._eval_episode_return = 0.
return obs
def step(self, action: np.array):
action = to_ndarray(action)
obs, rew, done, info = self._env.step(int(action))
self._eval_episode_return += rew
obs = to_ndarray(obs).astype('float32')
rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,)
if done:
info['eval_episode_return'] = self._eval_episode_return
return BaseEnvTimestep(obs, rew, done, info)
def _make_env(self, only_info=False):
return wrap_sokoban(
self._env_id,
norm_obs=self._cfg.get('norm_obs', EasyDict(use_norm=False, )),
norm_reward=self._cfg.get('norm_reward', EasyDict(use_norm=False, )),
only_info=only_info
)
def close(self) -> None:
if self._init_flag:
self._env.close()
self._init_flag = False
def enable_save_replay(self, replay_path) -> None:
if replay_path is None:
replay_path = './video'
self._save_replay = True
self._replay_path = replay_path
def __repr__(self) -> str:
return "DI-engine Sokoban Env({})".format(self._cfg.env_id)
@staticmethod
def create_collector_env_cfg(cfg: dict) -> List[dict]:
collector_cfg = copy.deepcopy(cfg)
collector_env_num = collector_cfg.pop('collector_env_num', 1)
return [collector_cfg for _ in range(collector_env_num)]
@staticmethod
def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
evaluator_cfg = copy.deepcopy(cfg)
evaluator_env_num = evaluator_cfg.pop('evaluator_env_num', 1)
evaluator_cfg.norm_reward = EasyDict(use_norm=False, )
return [evaluator_cfg for _ in range(evaluator_env_num)]
@property
def observation_space(self) -> gym.spaces.Space:
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
return self._action_space
@property
def reward_space(self) -> gym.spaces.Space:
return self._reward_space
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