Multi-agent Reinforcement Learning Paper Reading QPLEX

In the previous article, I shared the paper(you can follow the link below to recap!!!): Weighted QMIX: Expanding Monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning, which argues that the previous approaches, such as VDN and QMIX, can only factorize a little group of tasks, and proposed a new framework to overcome the issue. In this article, I gonna share another way to factorize any factorizable task, which is called QPLEX!!! In most of the multi-agent approaches, we tend to explore the popular paradigm of centralized training with decentralized execution(CTDE). In this paradigm, individual-Global-Max(IGM) principle plays an important role. However, lots of the methods tend to relax the IGM consistency so that they can achieve scalability.