Decentralized Policy

Decentralized policy optimization in multi-agent systems focuses on developing efficient algorithms where individual agents independently learn and adapt their policies to achieve a shared objective, without central coordination. Current research emphasizes improving the scalability and convergence of these algorithms, exploring model-based approaches alongside model-free methods like independent policy optimization and developing novel algorithms with provable convergence guarantees, such as those based on approximate linear programming or mirror descent. These advancements are significant for tackling the computational challenges inherent in large-scale multi-agent systems and have implications for diverse applications, including cooperative robotics, traffic control, and distributed resource management.