Spaces:
Sleeping
Sleeping
| import copy | |
| import random | |
| import numpy as np | |
| import gym | |
| from typing import Any, Dict, Optional, Union, List | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.utils import ENV_REGISTRY | |
| from ding.torch_utils import to_ndarray | |
| class BitFlipEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._n_bits = cfg.n_bits | |
| self._state = np.zeros(self._n_bits) | |
| self._goal = np.zeros(self._n_bits) | |
| self._curr_step = 0 | |
| self._maxsize = self._n_bits | |
| self._eval_episode_return = 0 | |
| self._observation_space = gym.spaces.Box(low=0, high=1, shape=(2 * self._n_bits, ), dtype=np.float32) | |
| self._action_space = gym.spaces.Discrete(self._n_bits) | |
| self._reward_space = gym.spaces.Box(low=0.0, high=1.0, shape=(1, ), dtype=np.float32) | |
| def reset(self) -> np.ndarray: | |
| self._curr_step = 0 | |
| self._eval_episode_return = 0 | |
| if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed: | |
| random_seed = 100 * random.randint(1, 1000) | |
| np.random.seed(self._seed + random_seed) | |
| elif hasattr(self, '_seed'): | |
| np.random.seed(self._seed) | |
| self._state = np.random.randint(0, 2, size=(self._n_bits, )).astype(np.float32) | |
| self._goal = np.random.randint(0, 2, size=(self._n_bits, )).astype(np.float32) | |
| while (self._state == self._goal).all(): | |
| self._goal = np.random.randint(0, 2, size=(self._n_bits, )).astype(np.float32) | |
| obs = np.concatenate([self._state, self._goal], axis=0) | |
| return obs | |
| def close(self) -> None: | |
| pass | |
| def check_success(self, state: np.ndarray, goal: np.ndarray) -> bool: | |
| return (self._state == self._goal).all() | |
| def seed(self, seed: int, dynamic_seed: bool = True) -> None: | |
| self._seed = seed | |
| self._dynamic_seed = dynamic_seed | |
| random.seed(self._seed) | |
| def step(self, action: np.ndarray) -> BaseEnvTimestep: | |
| self._state[action] = 1 - self._state[action] | |
| if self.check_success(self._state, self._goal): | |
| rew = np.array([1]).astype(np.float32) | |
| done = True | |
| else: | |
| rew = np.array([0]).astype(np.float32) | |
| done = False | |
| self._eval_episode_return += float(rew) | |
| if self._curr_step >= self._maxsize - 1: | |
| done = True | |
| info = {} | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| self._curr_step += 1 | |
| obs = np.concatenate([self._state, self._goal], axis=0) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def random_action(self) -> np.ndarray: | |
| random_action = self.action_space.sample() | |
| random_action = to_ndarray([random_action], dtype=np.int64) | |
| return random_action | |
| def observation_space(self) -> gym.spaces.Space: | |
| return self._observation_space | |
| def action_space(self) -> gym.spaces.Space: | |
| return self._action_space | |
| def reward_space(self) -> gym.spaces.Space: | |
| return self._reward_space | |
| def __repr__(self) -> str: | |
| return "DI-engine BitFlip Env({})".format('bitflip') | |