Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling

Researchers propose ALTERNATING-MARL, a new framework for cooperative multi-agent reinforcement learning that enables a global agent to learn with massive populations under communication constraints. The method achieves approximate Nash equilibrium convergence while only observing a subset of local agent states, with applications in multi-robot control and federated optimization.