When to Align: Dynamic Behavior Consistency for Multiagent Systems via Intrinsic Rewards.

IF 8.9 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kunyang Lin,Yufeng Wang,Peihao Chen,Runhao Zeng,Yinjie Lei,Siyuan Zhou,Qing Du,Mingkui Tan,Chuang Gan
{"title":"When to Align: Dynamic Behavior Consistency for Multiagent Systems via Intrinsic Rewards.","authors":"Kunyang Lin,Yufeng Wang,Peihao Chen,Runhao Zeng,Yinjie Lei,Siyuan Zhou,Qing Du,Mingkui Tan,Chuang Gan","doi":"10.1109/tnnls.2025.3598301","DOIUrl":null,"url":null,"abstract":"In multiagent systems, learning optimal behavior policies for individual agents remains a challenging yet crucial task. While recent research has made strides in this area, the issue of when agents should maintain consistent behaviors with one another is still not adequately addressed. This article proposes a novel approach to enable agents to autonomously decide whether their behaviors should align with those of their peers by leveraging intrinsic rewards to optimize their policies. We define behavior consistency as the divergence between the actions taken by two agents given the same observations. To encourage agents to be aware of each other's behaviors, we propose dynamic consistency-based intrinsic reward (DCIR), which guides agents in determining when to synchronize their behaviors. In addition, we introduce a dynamic scaling network (DSN) that provides learnable scaling factors at each time step, enabling agents to dynamically decide the extent of rewarding consistent behavior. Our method is evaluated on environments including Multiagent Particle, Google Research Football, and StarCraft II Micromanagement. Experimental results demonstrate its effectiveness in learning optimal policies.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"12 1","pages":""},"PeriodicalIF":8.9000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on neural networks and learning systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/tnnls.2025.3598301","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

In multiagent systems, learning optimal behavior policies for individual agents remains a challenging yet crucial task. While recent research has made strides in this area, the issue of when agents should maintain consistent behaviors with one another is still not adequately addressed. This article proposes a novel approach to enable agents to autonomously decide whether their behaviors should align with those of their peers by leveraging intrinsic rewards to optimize their policies. We define behavior consistency as the divergence between the actions taken by two agents given the same observations. To encourage agents to be aware of each other's behaviors, we propose dynamic consistency-based intrinsic reward (DCIR), which guides agents in determining when to synchronize their behaviors. In addition, we introduce a dynamic scaling network (DSN) that provides learnable scaling factors at each time step, enabling agents to dynamically decide the extent of rewarding consistent behavior. Our method is evaluated on environments including Multiagent Particle, Google Research Football, and StarCraft II Micromanagement. Experimental results demonstrate its effectiveness in learning optimal policies.
何时对齐:通过内在奖励的多智能体系统的动态行为一致性。
在多智能体系统中,学习个体智能体的最优行为策略仍然是一项具有挑战性但又至关重要的任务。虽然最近的研究在这一领域取得了长足的进步,但代理之间何时应该保持一致的行为仍然没有得到充分的解决。本文提出了一种新颖的方法,通过利用内在奖励来优化策略,使代理能够自主决定他们的行为是否应该与同伴的行为保持一致。我们将行为一致性定义为给定相同观察值的两个主体所采取的行动之间的分歧。为了鼓励智能体意识到彼此的行为,我们提出了基于动态一致性的内在奖励(DCIR),它指导智能体决定何时同步他们的行为。此外,我们引入了一个动态缩放网络(DSN),该网络在每个时间步提供可学习的缩放因子,使智能体能够动态决定奖励一致行为的程度。我们的方法在Multiagent Particle、谷歌Research Football和星际争霸II微管理等环境中进行了评估。实验结果证明了该方法在学习最优策略方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信