Learning Superior Cooperative Policy in Adversarial Multi-Team Reinforcement Learning

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.