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from typing import Any, List, Union, Optional
from easydict import EasyDict
import time
import gym
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.envs.common.env_element import EnvElement, EnvElementInfo
from ding.torch_utils import to_ndarray, to_list
from ding.utils import ENV_REGISTRY, deep_merge_dicts
@ENV_REGISTRY.register('procgen')
class ProcgenEnv(BaseEnv):
#If control_level is True, you can control the specific level of the generated environment by controlling start_level and num_level.
config = dict(
control_level=True,
start_level=0,
num_levels=0,
env_id='coinrun',
)
def __init__(self, cfg: dict) -> None:
cfg = deep_merge_dicts(EasyDict(self.config), cfg)
self._cfg = cfg
self._seed = 0
self._init_flag = False
self._observation_space = gym.spaces.Box(
low=np.zeros(shape=(3, 64, 64)), high=np.ones(shape=(3, 64, 64)) * 255, shape=(3, 64, 64), dtype=np.float32
)
self._action_space = gym.spaces.Discrete(15)
self._reward_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(1, ), dtype=np.float32)
self._control_level = self._cfg.control_level
self._start_level = self._cfg.start_level
self._num_levels = self._cfg.num_levels
self._env_name = 'procgen:procgen-' + self._cfg.env_id + '-v0'
# In procgen envs, we use seed to control level, and fix the numpy seed to 0
np.random.seed(0)
def reset(self) -> np.ndarray:
if not self._init_flag:
if self._control_level:
self._env = gym.make(self._env_name, start_level=self._start_level, num_levels=self._num_levels)
else:
self._env = gym.make(self._env_name, start_level=0, num_levels=1)
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._env.close()
if self._control_level:
self._env = gym.make(self._env_name, start_level=self._start_level, num_levels=self._num_levels)
else:
self._env = gym.make(self._env_name, start_level=self._seed + np_seed, num_levels=1)
elif hasattr(self, '_seed'):
self._env.close()
if self._control_level:
self._env = gym.make(self._env_name, start_level=self._start_level, num_levels=self._num_levels)
else:
self._env = gym.make(self._env_name, start_level=self._seed, num_levels=1)
self._eval_episode_return = 0
obs = self._env.reset()
obs = to_ndarray(obs)
obs = np.transpose(obs, (2, 0, 1))
obs = obs.astype(np.float32)
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
def step(self, action: np.ndarray) -> BaseEnvTimestep:
assert isinstance(action, np.ndarray), type(action)
if action.shape == (1, ):
action = action.squeeze() # 0-dim array
obs, rew, done, info = self._env.step(action)
self._eval_episode_return += rew
if done:
info['eval_episode_return'] = self._eval_episode_return
obs = to_ndarray(obs)
obs = np.transpose(obs, (2, 0, 1))
obs = obs.astype(np.float32)
rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,)
rew = rew.astype(np.float32)
return BaseEnvTimestep(obs, rew, bool(done), info)
@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 CoinRun Env"
def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
if replay_path is None:
replay_path = './video'
self._replay_path = replay_path
self._env = gym.wrappers.Monitor(
self._env, self._replay_path, video_callable=lambda episode_id: True, force=True
)
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