CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
赵冬斌; 邵坤; 朱圆恒; 李栋; 陈亚冉; 王海涛; 刘德荣; 周彤; 王成红
Source Publication控制理论与应用
Other AbstractDeep 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 mech-anism 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 deve-loped 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.
Keyword深度强化学习 初弈号 深度学习 强化学习 人工智能
Document Type期刊论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
赵冬斌,邵坤,朱圆恒,等. 深度强化学习综述:兼论计算机围棋的发展[J]. 控制理论与应用,2016,33(6):701-717.
APA 赵冬斌.,邵坤.,朱圆恒.,李栋.,陈亚冉.,...&王成红.(2016).深度强化学习综述:兼论计算机围棋的发展.控制理论与应用,33(6),701-717.
MLA 赵冬斌,et al."深度强化学习综述:兼论计算机围棋的发展".控制理论与应用 33.6(2016):701-717.
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