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import copy
from typing import List, Union, Optional
import gym
import numpy as np
from easydict import EasyDict
from ding.envs.env.base_env import BaseEnv, BaseEnvTimestep
from ding.torch_utils import to_ndarray
from ding.utils import ENV_REGISTRY
@ENV_REGISTRY.register('cliffwalking')
class CliffWalkingEnv(BaseEnv):
def __init__(self, cfg: dict) -> None:
self._cfg = EasyDict(
env_id='CliffWalking',
render_mode='rgb_array',
max_episode_steps=300, # default max trajectory length to truncate possible infinite attempts
)
self._cfg.update(cfg)
self._init_flag = False
self._replay_path = None
self._observation_space = gym.spaces.Box(low=0, high=1, shape=(48, ), dtype=np.float32)
self._env = gym.make(
"CliffWalking", render_mode=self._cfg.render_mode, max_episode_steps=self._cfg.max_episode_steps
)
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
)
def reset(self) -> np.ndarray:
if not self._init_flag:
self._env = gym.make(
"CliffWalking", render_mode=self._cfg.render_mode, max_episode_steps=self._cfg.max_episode_steps
)
self._init_flag = True
if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
dy_seed = self._seed + 100 * np.random.randint(1, 1000)
self._env.seed(dy_seed)
elif hasattr(self, '_seed'):
self._env.seed(self._seed)
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='cliffwalking-{}'.format(id(self))
)
obs = self._env.reset()
obs_encode = self._encode_obs(obs)
self._eval_episode_return = 0.
return obs_encode
def close(self) -> None:
try:
self._env.close()
del self._env
except:
pass
def seed(self, seed: int, dynamic_seed: bool = True) -> None:
self._seed = seed
self._dynamic_seed = dynamic_seed
np.random.seed(seed)
def step(self, action: Union[int, np.ndarray]) -> BaseEnvTimestep:
if isinstance(action, np.ndarray):
if action.shape == (1, ):
action = action.squeeze() # 0-dim array
action = action.item()
obs, reward, done, info = self._env.step(action)
obs_encode = self._encode_obs(obs)
self._eval_episode_return += reward
reward = to_ndarray([reward], dtype=np.float32)
if done:
info['eval_episode_return'] = self._eval_episode_return
return BaseEnvTimestep(obs_encode, reward, 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:
random_action = self.action_space.sample()
if isinstance(random_action, int):
random_action = to_ndarray([random_action], dtype=np.int64)
return random_action
def _encode_obs(self, obs) -> np.ndarray:
onehot = np.zeros(48, dtype=np.float32)
onehot[int(obs)] = 1
return onehot
@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
def __repr__(self) -> str:
return "DI-engine CliffWalking Env"
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