深度强化学习综述: 兼论计算机围棋的发展

Review of deep reinforcement learning and discussions on the development of computer Go

中文关键词  深度强化学习  初弈号  深度学习  强化学习  人工智能

英文关键词  deep reinforcement learning  AlphaGo  deep learning  reinforcement learning  artificial intelligenc

中文摘要

      深度强化学习将深度学习的感知能力和强化学习的决策能力相结合, 可以直接根据输入的图像进行控制, 是一种更接近人类思维方式的人工智能方法. 自提出以来, 深度强化学习在理论和应用方面均取得了显著的成果. 尤其是谷歌深智(DeepMind)团队基于深度强化学习方法研发的计算机围棋“初弈号–AlphaGo”, 在2016年3月以 4:1的大比分战胜了世界围棋顶级选手李世石(Lee Sedol), 成为人工智能历史上一个新里程碑. 为此, 本文综述深度 强化学习的发展历程, 兼论计算机围棋的历史, 分析算法特性, 探讨未来的发展趋势和应用前景, 期望能为控制理论 与应用新方向的发展提供有价值的参考.

英文摘要

      Deep reinforcement learning which incorporates both the advantages of the perception of deep learning and the decision making of reinforcement learning is able to output control signal directly based on input images. This mechanism makes the artificial intelligence much close to human thinking modes. Deep reinforcement learning has achieved remarkable success in terms of theory and application since it is proposed. ‘Chuyihao–AlphaGo’, a computer Go developed by Google DeepMind, based on deep reinforcement learning, beat the world’s top Go player Lee Sedol 4:1 in March 2016. This becomes a new milestone in artificial intelligence history. This paper surveys the development course of deep reinforcement learning, reviews the history of computer Go concurrently, analyzes the algorithms features, and discusses the research directions and application areas, in order to provide a valuable reference to the development of control theory and applications in a new direction.