掌握阿塔里,围棋,象棋和冲,通过学习模型进行计划
Julian Schrittwieser1, Ioannis Antonoglou1,2, Thomas Hubert1
1DeepMind, London, UK.
Nature
|December 28, 2020
概括
MuZero算法将基于树的搜索与学习模型相结合,在不了解环境动态的情况下在复杂领域实现超人性能. 这种人工智能突破在阿塔里游戏中脱而出, 并与围棋和国际象棋中的顶级人工智能相匹配.
科学领域:
- 人工智能
- 机器学习
- 强化学习
背景情况:
- 对于人工智能代理来说, 计划能力至关重要.
- 在拥有完美的模拟器 (如象棋,围棋) 的领域,基于树的规划是卓越的.
- 现实世界的问题往往涉及到复杂的,未知的环境动态, 阻碍了传统的规划方法.
研究的目的:
- 介绍MuZero算法, 这是一种人工智能规划的新方法.
- 证明MuZero能够在没有先前了解环境动态的情况下实现高性能.
- 评估MuZero在不同和视觉复杂领域的有效性.
主要方法:
- MuZero将基于树的搜索与学习模型相结合.
- 算法学习了一个可代的模型, 预测政策,价值和奖励.
- 这种学习模型支持未知的环境中的规划.
主要成果:
- 在57款Atari游戏中,MuZero实现了最先进的性能,而基于模型的规划在历史上一直处于困境之中.
- MuZero在围棋,国际象棋和shogi中与阿尔法Zero的超人表现相匹配, 尽管他并不了解游戏规则.
- 该算法在视觉复杂和动态环境中表现出强大的规划能力.
结论:
- MuZero算法代表了人工智能规划的重大进展.
- MuZero的学习模型方法克服了未知的环境中的基于模型的规划局限性.
- 这种方法为开发能够解决复杂现实问题的智能代理提供了一个强大的新工具.
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