CASIA OpenIR  > 复杂系统认知与决策实验室  > 飞行器智能技术
Learning Superior Cooperative Policy in Competitive Multi-team Reinforcement Learning
Qingxu Fu1,2; Tenghai Qiu1,2; Zhiqiang Pu1,2; Jianqiang Yi1,2; Xiaolin Ai1,2; Wanmai Yuan1,2
2023-06
Conference Name2023 International Joint Conference on Neural Networks
Conference Date2023-6
Conference PlaceGold Coast, Australia
Abstract

Multi-agent Reinforcement Learning (MARL) has become a powerful tool for addressing multi-agent challenges. Existing studies have explored numerous models to use MARL to solve single-team cooperation (competition) problems and adversarial problems with opponents controlled by static knowledge-based policies. However, most studies in the literature often ignore adversarial multi-team problems involving dynamically evolving opponents. We investigate adversarial multi-team problems where all participating teams use MARL learners to learn policies against each other. Two objectives are achieved in this study. Firstly, we design an adversarial team-versus-team learning framework to generate cooperative multi-agent policies to compete against opponents without preprogrammed opponent partners or any supervision. Secondly, we explore the key factors to achieve win-rate superiority during dynamic competitions. Then we put forward a novel FeedBack MARL (FBMARL) algorithm that takes advantage of feedback loops to adjust optimizer hyper-parameters based on real-time game statistics. Finally, the effectiveness of our FBMARL model is tested in a benchmark environment named Multi-Team Decentralized Collective Assault (MT-DCA). The results demonstrate that our feedback MARL model can achieve superior performance over baseline competitor MARL learners in 2-team and 3-team dynamic competitions.

Indexed ByEI
Sub direction classification决策智能理论与方法
planning direction of the national heavy laboratory多智能体决策
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57225
Collection复杂系统认知与决策实验室_飞行器智能技术
Affiliation1.80146-中国科学院自动化研究所
2.80170-中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qingxu Fu,Tenghai Qiu,Zhiqiang Pu,et al. Learning Superior Cooperative Policy in Competitive Multi-team Reinforcement Learning[C],2023.
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