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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
Conference Name2023 International Joint Conference on Neural Networks
Conference Date2023-6
Conference PlaceGold Coast, Australia

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
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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