模糊多智能体协同q学习

2005 IEEE International Conference on Information Acquisition Pub Date : 1900-01-01 DOI:10.1109/ICIA.2005.1635080

Dongbing Gu, Huosheng Hu

{"title":"模糊多智能体协同q学习","authors":"Dongbing Gu, Huosheng Hu","doi":"10.1109/ICIA.2005.1635080","DOIUrl":null,"url":null,"abstract":"This paper presents a cooperative reinforcement learning algorithm of multi-agent systems. The cooperative behaviour is established within a leader-following framework. Specifically, the cooperative dynamics is modelled as a Stackelberg game. Based on the equilibrium definition of the Stackelberg game, a leader-following Q-learning algorithm is developed. The algorithm is generalised over continuous state space by using fuzzy logic.","PeriodicalId":136611,"journal":{"name":"2005 IEEE International Conference on Information Acquisition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Fuzzy multi-agent cooperative Q-learning\",\"authors\":\"Dongbing Gu, Huosheng Hu\",\"doi\":\"10.1109/ICIA.2005.1635080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a cooperative reinforcement learning algorithm of multi-agent systems. The cooperative behaviour is established within a leader-following framework. Specifically, the cooperative dynamics is modelled as a Stackelberg game. Based on the equilibrium definition of the Stackelberg game, a leader-following Q-learning algorithm is developed. The algorithm is generalised over continuous state space by using fuzzy logic.\",\"PeriodicalId\":136611,\"journal\":{\"name\":\"2005 IEEE International Conference on Information Acquisition\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Information Acquisition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIA.2005.1635080\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Information Acquisition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIA.2005.1635080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

摘要

提出了一种多智能体系统的协同强化学习算法。合作行为是在领导跟随框架内建立起来的。具体来说，合作动力学被建模为Stackelberg博弈。基于Stackelberg博弈的均衡定义，提出了一种领导者跟随q学习算法。该算法利用模糊逻辑在连续状态空间上进行推广。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Fuzzy multi-agent cooperative Q-learning

This paper presents a cooperative reinforcement learning algorithm of multi-agent systems. The cooperative behaviour is established within a leader-following framework. Specifically, the cooperative dynamics is modelled as a Stackelberg game. Based on the equilibrium definition of the Stackelberg game, a leader-following Q-learning algorithm is developed. The algorithm is generalised over continuous state space by using fuzzy logic.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2005 IEEE International Conference on Information Acquisition

自引率

0.00%

发文量