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中文摘要：
基于增强学习的多机器人系统优化控制是近年来机器人学与分布式人工智能的前沿研究领域.多机器人系统具有分布、异构和高维连续空间等特性,使得面向多机器人系统的增强学习的研究面临着一系列挑战,为此,对其相关理论和算法的研究进展进行了系统综述.首先,阐述了多机器人增强学习的基本理论模型和优化目标;然后,在对已有学习算法进行对比分析的基础上,重点探讨了多机器人增强学习理论与应用研究中的困难和求解思路,给出了若干典型问题和应用实例;最后,对相关研究进行了总结和展望.
英文摘要：
Multi-robot optimization control based on reinforcement learning is a research frontier of robotics and distributed artificial intelligence in recent years.Some characteristics in multi-robot systems,such as distribution,heterogeneity and high-dimensional continuity,lead to a series of challenges in theoretical and methodological research for multi-robot reinforcement learning.Therefore,recent advances of multi-robot reinforcement learning are systematically surveyed.Firstly,the fundamental theoretical models and optimization objectives are analyzed.Based on a contrastive analysis for existing algorithms,the difficulties in theoretical research and implementations are discussed,and the possible solutions are summarized in detail.Several benchmark problems and applications are listed.Finally,current work and future research directions are concluded.
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