Balance of exploration and exploitation: Non-cooperative game-driven evolutionary reinforcement learning

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
{"title":"Balance of exploration and exploitation: Non-cooperative game-driven evolutionary reinforcement learning","authors":"","doi":"10.1016/j.swevo.2024.101759","DOIUrl":null,"url":null,"abstract":"<div><div>In a complex and dynamic environment, it becomes difficult to solve problems with a single policy updating mode. Although evolutionary reinforcement learning partially addresses this issue, it fails to consider the real-time status of the agent, resulting in inflexible policy updating modes that can negatively affect algorithm performance. To address this issue, we propose an evolutionary reinforcement learning based on non-cooperative games that leverages the benefits of various algorithms in cooperative and non-cooperative settings. Firstly, a competition framework is established between the evolutionary algorithm and reinforcement learning using a non-cooperative game. The policy updating mode is dynamically selected based on the game’s outcome, ensuring diverse algorithms through differentiated population evolution guided by Nash equilibrium. Secondly, the evolutionary algorithm collaborates with the non-cooperative game to establish a cooperative framework that balances exploration and convergence. Since the exploration of the evolutionary algorithm does not rely on the environment, it is used to guide the non-cooperative game, which helps the algorithm successfully overcome the local optimum. This synergy significantly enhances algorithm performance. Experimental results demonstrate that the proposed algorithm outperforms individual algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650224002979","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 a complex and dynamic environment, it becomes difficult to solve problems with a single policy updating mode. Although evolutionary reinforcement learning partially addresses this issue, it fails to consider the real-time status of the agent, resulting in inflexible policy updating modes that can negatively affect algorithm performance. To address this issue, we propose an evolutionary reinforcement learning based on non-cooperative games that leverages the benefits of various algorithms in cooperative and non-cooperative settings. Firstly, a competition framework is established between the evolutionary algorithm and reinforcement learning using a non-cooperative game. The policy updating mode is dynamically selected based on the game’s outcome, ensuring diverse algorithms through differentiated population evolution guided by Nash equilibrium. Secondly, the evolutionary algorithm collaborates with the non-cooperative game to establish a cooperative framework that balances exploration and convergence. Since the exploration of the evolutionary algorithm does not rely on the environment, it is used to guide the non-cooperative game, which helps the algorithm successfully overcome the local optimum. This synergy significantly enhances algorithm performance. Experimental results demonstrate that the proposed algorithm outperforms individual algorithms.
探索与开发的平衡:非合作博弈驱动的进化强化学习
在复杂多变的环境中,单一的策略更新模式很难解决问题。虽然进化强化学习能部分解决这个问题,但它没有考虑到代理的实时状态,导致策略更新模式不灵活,从而对算法性能产生负面影响。为了解决这个问题,我们提出了一种基于非合作博弈的进化强化学习方法,它充分利用了合作和非合作环境中各种算法的优势。首先,利用非合作博弈在进化算法和强化学习之间建立一个竞争框架。根据博弈结果动态选择策略更新模式,通过纳什均衡指导下的种群差异化进化确保算法的多样性。其次,进化算法与非合作博弈合作,建立了一个兼顾探索和收敛的合作框架。由于进化算法的探索并不依赖于环境,它被用来指导非合作博弈,从而帮助算法成功克服局部最优。这种协同作用大大提高了算法性能。实验结果表明,所提出的算法优于单个算法。
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
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
×
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:481959085
Book学术官方微信