Stateful Value Factorization in Multi-Agent Reinforcement Learning: Bridging Theory and Practice

This work addresses the mismatch between the theoretical frameworks and practical implementations of value function factorization in multi-agent reinforcement learning. It proposes a novel efficient factorization algorithm, DuelMIX, that learns distinct per-agent utility estimators to improve performance and achieve full expressiveness.