在没有人类知识的情况下掌握围棋游戏
David Silver1, Julian Schrittwieser1, Karen Simonyan1
1DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Nature
|October 21, 2017
概括
一个新的人工智能算法,AlphaGo Zero, 完全通过强化学习来学习, 没有人类数据. 通过对自己玩的数据进行训练, 在围棋游戏中实现了超人性能.
科学领域:
- 人工智能
- 强化学习
- 游戏理论
背景情况:
- 人工智能 (AI) 的一个关键目标是创建能够在复杂领域实现超人类能力的算法.
- 像AlphaGo这样的先前人工智能系统利用了来自人类专家数据的监督学习和自动游戏的强化学习.
- 然而,这些系统仍然依赖于人类的知识和指导.
研究的目的:
- 开发一种完全通过强化学习学习的人工智能算法, 没有任何人类数据或领域专业知识.
- 证明人工智能可以成为自己的老师, 通过自我游戏反复提高自己的表现.
- 通过一种新的,自学的人工智能方法在围棋游戏中实现超人性能.
主要方法:
- 这项研究引入了一种算法AlphaGo Zero,
- 一个神经网络被训练来预测AlphaGo Zero自己的动作选择和游戏结果.
- 这种神经网络增强了树搜索算法,导致后续代中改进的移动选择和更强的自我玩法.
主要成果:
- 在没有人类数据,指导或游戏规则之外的领域知识的情况下,
- 算法在围棋游戏中取得了超人性能.
- 阿尔法戈零号以100比0击败了之前的冠军级别的阿尔法戈.
结论:
- 只有强化学习,没有人类数据,就足以让人工智能在具有挑战性的领域实现超人性能.
- 自动游戏和代自我改进是人工智能学习和发展的强大机制.
- 它代表了人工智能的重大进步,
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