Coordination Between Individual Agents in Multi-Agent Reinforcement Learning

被引:0
|
作者
Zhang, Yang [1 ]
Yang, Qingyu [1 ,2 ,3 ]
An, Dou [1 ,2 ,3 ]
Zhang, Chengwei [4 ]
机构
[1] Xi An Jiao Tong Univ, Fac Elect & Informat, Xian, Peoples R China
[2] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn SKLMSE, Xian, Peoples R China
[3] Xi An Jiao Tong Univ, Key Lab Intelligent Networks & Network Secur, Minist Educ MOE, Xian, Peoples R China
[4] Dalian Maritime Univ, Dalian, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The existing multi-agent reinforcement learning methods (MARL) for determining the coordination between agents focus on either global-level or neighborhood-level coordination between agents. However the problem of coordination between individual agents is remain to be solved. It is crucial for learning an optimal coordinated policy in unknown multi-agent environments to analyze the agent's roles and the correlation between individual agents. To this end, in this paper we propose an agent-level coordination based MARL method. Specifically, it includes two parts in our method. The first is correlation analysis between individual agents based on the Pearson, Spearman, and Kendall correlation coefficients; And the second is an agent-level coordinated training framework where the communication message between weakly correlated agents is dropped out, and a correlation based reward function is built. The proposed method is verified in four mixed cooperative-competitive environments. The experimental results show that the proposed method outperforms the state-of-the-art MARL methods and can measure the correlation between individual agents accurately.
引用
收藏
页码:11387 / 11394
页数:8
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