控制理论与应用2017，Vol.34Issue(12)：1529-1546,18.DOI:10.7641/CTA.2017.70808
深度强化学习进展:从AlphaGo到AlphaGo Zero
Recent progress of deep reinforcement learning:from AlphaGo to AlphaGo Zero
摘要
Abstract
In the early 2016,the defeat of Lee Sedol by AlphaGo became the milestone of artificial intelligence.Since then,deep reinforcement learning(DRL),which is the core technique of AlphaGo,has received widespread attention,and has gained fruitful results in both theory and applications.In the sequel,AlphaGo Zero,a simplified version of AlphaGo, masters the game of Go by self-play without human knowledge.As a result,AlphaGo Zero completely surpasses AlphaGo, and enriches humans'understanding of DRL.DRL combines the advantages of deep learning and reinforcement learning, so it is able to perform well in high-dimensional state-action space, with an end-to-end structure combining perception and decision together.In this paper, we present a survey on the remarkable process made by DRL from AlphaGo to AlphaGo Zero.We first review the main algorithms that contribute to the great success of DRL,including DQN,A3C, policy-gradient,and other algorithms and their extensions.Then,detailed introduction and discussion on AlphaGo Zero are given and its great promotion on artificial intelligence is also analyze.The progress of applications with DRL in such areas as games,robotics,natural language processing,smart driving,intelligent health care,and related resources are also presented.In the end,we discuss the future development of DRL,and the inspiration on other potential areas related to artificial intelligence.关键词
深度强化学习/AlphaGoZero/深度学习/强化学习/人工智能Key words
deep reinforcement learning/AlphaGo Zero/deep learning/reinforcement learning/artificial intelligence分类
信息技术与安全科学引用本文复制引用
唐振韬,邵坤,赵冬斌,朱圆恒..深度强化学习进展:从AlphaGo到AlphaGo Zero[J].控制理论与应用,2017,34(12):1529-1546,18.基金项目
国家自然科学基金项目(61603382,61573353,61533017)资助.Supported by the National Natural Science Foundation of China(61603382,61573353,61533017). （61603382,61573353,61533017）
