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from typing import Any, List, Union, Sequence
import copy
import gym
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.utils import ENV_REGISTRY
from ding.torch_utils import to_ndarray, to_list
from .atari_wrappers import wrap_deepmind
from pprint import pprint
def PomdpEnv(cfg, only_info=False):
'''
For debug purpose, create an env follow openai gym standard so it can be widely test by
other library with same environment setting in DI-engine
env = PomdpEnv(cfg)
obs = env.reset()
obs, reward, done, info = env.step(action)
'''
env = wrap_deepmind(
cfg.env_id,
frame_stack=cfg.frame_stack,
episode_life=cfg.is_train,
clip_rewards=cfg.is_train,
warp_frame=cfg.warp_frame,
use_ram=cfg.use_ram,
render=cfg.render,
pomdp=cfg.pomdp,
only_info=only_info,
)
return env
@ENV_REGISTRY.register('pomdp')
class PomdpAtariEnv(BaseEnv):
def __init__(self, cfg: dict) -> None:
self._cfg = cfg
self._init_flag = False
def reset(self) -> Sequence:
if not self._init_flag:
self._env = self._make_env(only_info=False)
self._init_flag = True
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)
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)
action = action.item()
obs, rew, done, info = self._env.step(action)
self._eval_episode_return += rew
obs = to_ndarray(obs)
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_deepmind(
self._cfg.env_id,
episode_life=self._cfg.is_train,
clip_rewards=self._cfg.is_train,
pomdp=self._cfg.pomdp,
frame_stack=self._cfg.frame_stack,
warp_frame=self._cfg.warp_frame,
use_ram=self._cfg.use_ram,
only_info=only_info,
)
def __repr__(self) -> str:
return "DI-engine POMDP Atari Env({})".format(self._cfg.env_id)
@staticmethod
def create_collector_env_cfg(cfg: dict) -> List[dict]:
collector_env_num = cfg.pop('collector_env_num', 1)
cfg = copy.deepcopy(cfg)
cfg.is_train = True
return [cfg for _ in range(collector_env_num)]
@staticmethod
def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
evaluator_env_num = cfg.pop('evaluator_env_num', 1)
cfg = copy.deepcopy(cfg)
cfg.is_train = False
return [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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