Co-Evolving Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation

Multi-Agent Reinforcement Learning (MARL) is extensively utilized for addressing intricate tasks that involve cooperation and competition among agents in Multi-Agent Systems (MAS). However, learning such tasks from scratch is challenging and often unfeasible, especially for MASs with a large number of agents. Hence, leveraging knowledge from prior experiences can effectively expedite the MARL learning process. Prior work has shown that we successfully facilitated transfer learning for MARL by consolidating various state spaces into fixed-size inputs, enabling a single unified deep-learning policy applicable to several scenarios within the StarCraft Multi-Agent Challenge (SMAC) environment. In this study, we expand SMAC to Multi-Player enabled SMAC (MP-SMAC) by enabling the dynamic selection of training opponents and introducing a co-evolving MARL framework, which creates a co-evolutionary arena where multiple policies learn simultaneously. Our arena comprised the simultaneous training of multiple policies in diverse scenarios, pitting them against both static AI opponents and their peers within MP-SMAC. Furthermore, we integrate co-evolution with curriculum transfer learning into Co-MACTRL framework, enabling our MARL policies to systematically acquire knowledge and skills across predetermined scenarios organized by varying difficulty levels, including evolving opponents. The results revealed significant enhancements in MARL learning performance, demonstrating the advantage of leveraging the co-evolving opponents and maneuvering skills obtained from different scenarios. Additionally, the Co-MACTRL learners consistently attained high performance across a range of SMAC scenarios, showcasing the robustness and generalizability of Co-MACTRL.