一个一般的强化学习算法,可以掌握象棋,shogi和Go通过自我游戏
David Silver1,2, Thomas Hubert3, Julian Schrittwieser3
1DeepMind, 6 Pancras Square, London N1C 4AG, UK. davidsilver@google.com dhcontact@google.com.
概括
新的人工智能算法AlphaZero使用强化学习在象棋和围棋等游戏中实现超人性能. 在没有人类领域知识的情况下, 它从自我游戏中学习, 通过随机游戏击败顶级程序.
科学领域:
- 人工智能
- 机器学习
- 游戏理论
背景情况:
- 国际象棋一直是人工智能研究的长期基准.
- 目前的 AI 国际象棋程序依赖于人类的专业知识和手工评估功能.
- 强化学习 (RL) 的近期进步在诸如围棋之类的复杂游戏中显得有前途.
研究的目的:
- 开发一种通用的人工智能算法,
- 在没有特定领域知识的情况下,通过自我游戏来证明强化学习的有效性.
主要方法:
- 该研究介绍了AlphaZero算法,这是一个统一的方法,将AlphaGo Zero方法概括起来.
- AlphaZero使用深度神经网络和蒙特卡罗树搜索,通过自我学习进行训练.
- 算法只提供游戏规则,从随机游戏开始.
主要成果:
- 阿尔法零在国际象棋,冲和围棋中取得了超人类的成绩.
- 算法在这些游戏中令人信服地击败了世界冠军级别的人工智能程序.
- 这证明了游戏AI的显著进步.
结论:
- 从自我游戏中进行强化学习是开发复杂战略领域超人人工智能的强大范式.
- 它代表了向更广泛的人工智能系统迈出的重要一步.
- 这种方法消除了人工智能游戏开发中广泛的领域特定工程的需要.
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