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多智能体深度强化学习的若干关键科学问题

孙长银 穆朝絮

孙长银, 穆朝絮. 多智能体深度强化学习的若干关键科学问题. 自动化学报, 2020, 46(7): 1301−1312 doi: 10.16383/j.aas.c200159
引用本文: 孙长银, 穆朝絮. 多智能体深度强化学习的若干关键科学问题. 自动化学报, 2020, 46(7): 1301−1312 doi: 10.16383/j.aas.c200159
Sun Chang-Yin, Mu Chao-Xu. Important scientific problems of multi-agent deep reinforcement learning. Acta Automatica Sinica, 2020, 46(7): 1301−1312 doi: 10.16383/j.aas.c200159
Citation: Sun Chang-Yin, Mu Chao-Xu. Important scientific problems of multi-agent deep reinforcement learning. Acta Automatica Sinica, 2020, 46(7): 1301−1312 doi: 10.16383/j.aas.c200159

多智能体深度强化学习的若干关键科学问题

doi: 10.16383/j.aas.c200159
基金项目: 科技部人工智能专项重大项目 (2018AAA0101400), 国家自然科学基金创新研究群体(61921004), 国家自然科学基金(61942301)资助
详细信息
    作者简介:

    孙长银:东南大学自动化学院教授. 主要研究方向为智能控制与优化, 强化学习, 神经网络, 数据驱动控制. 本文通信作者.E-mail: cysun@seu.edu.cn

    穆朝絮:天津大学电气自动化与信息工程学院教授. 主要研究方向为强化学习, 自适应学习系统, 非线性控制和优化.E-mail: cxmu@tju.edu.cn

Important Scientific Problems of Multi-Agent Deep Reinforcement Learning

Funds: Supported by Artificial Intelligence Major Project of the Ministry of Science and Technology of China (2018AAA0101400), National Natural Science Foundation of China for Creative Research Groups (61921004), National Natural Science Foundation of China (61942301)
  • 摘要: 强化学习作为一种用于解决无模型序列决策问题的方法已经有数十年的历史, 但强化学习方法在处理高维变量问题时常常会面临巨大挑战. 近年来, 深度学习迅猛发展, 使得强化学习方法为复杂高维的多智能体系统提供优化的决策策略、在充满挑战的环境中高效执行目标任务成为可能. 本文综述了强化学习和深度强化学习方法的原理, 提出学习系统的闭环控制框架, 分析了多智能体深度强化学习中存在的若干重要问题和解决方法, 包括多智能体强化学习的算法结构、环境非静态和部分可观性等问题, 对所调查方法的优缺点和相关应用进行分析和讨论. 最后提供多智能体深度强化学习未来的研究方向, 为开发更强大、更易应用的多智能体强化学习控制系统提供一些思路.
  • 图  1  强化学习的基本原理

    Fig.  1  Basic principles of reinforcement learning

    图  2  深度强化学习原理图

    Fig.  2  Schematic diagram of deep reinforcement learning

    图  3  学习系统闭环控制框架

    Fig.  3  Relearnware: closed-loop control framework of learning systems

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  • 收稿日期:  2020-03-25
  • 网络出版日期:  2020-07-24
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