The article introduces Prioritized Heterogeneous League Reinforcement Learning (PHLRL) to tackle challenges in large-scale heterogeneous multiagent systems. It discusses the importance of diverse agent types, the non-stationarity problem, and decentralized deployment. PHLRL maintains a league of policies to optimize future policy decisions and introduces prioritized advantage coefficients to address agent type imbalances. The article also presents a benchmark environment, Large-Scale Heterogeneous Cooperation (LSHC), to evaluate PHLRL's performance. Experimental results show PHLRL outperforms state-of-the-art methods in LSHC. The paper concludes by discussing the scalability and effectiveness of PHLRL in solving heterogeneous multiagent challenges.
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by Qingxu Fu,Zh... at arxiv.org 03-28-2024
https://arxiv.org/pdf/2403.18057.pdfDeeper Inquiries