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