Knowledge Commons of Institute of Automation,CAS
Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games | |
Li WF(李伟凡); Zhu YH(朱圆恒); Zhao DB(赵冬斌) | |
2019-12 | |
会议名称 | 2019 IEEE Symposium Series on Computational Intelligence |
会议日期 | 2019-12-6 |
会议地点 | Xiamen, China |
出版者 | IEEE |
摘要 | Non-stationary environment is general in real environment, including adversarial environment and multi-agent problem. Multi-agent environment is a typical non-stationary environment. Each agent of the shared environment must learn a efficient interaction for maximizing the expected reward. Independent reinforcement learning (InRL) is the simplest form in which each agent treats other agents as part of environment. In this paper, we present Max-Mean-Learning-Win-or-LearnFast (MML-WoLF), which is an independent on-policy learning algorithm based on reinforcement clustering. A variational autoencoder method based on reinforcement learning is proposed to extract features for unsupervised clustering. Based on clustering results, MML-WoLF uses statistics and the dominated factor to calculate the values of the states that belong to a certain category. The agent policy is iteratively updated by the value. We apply our algorithm to multi-agent problems including matrixgame, grid world, and continuous world game. The clustering results are able to show the strategies distribution under the agent's current policy. The experiment results suggest that our method significantly improves average performance over other independent learning algorithms in multi-agent problems. |
关键词 | reinforcement learning unsupervised clustering matrix game |
七大方向——子方向分类 | 强化与进化学习 |
国重实验室规划方向分类 | 多智能体决策 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52212 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Zhu YH(朱圆恒) |
作者单位 | State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li WF,Zhu YH,Zhao DB. Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games[C]:IEEE,2019. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Multi-Agent_Reinforc(488KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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