Breakthrough the Suboptimal Stable Point in Value-Factorization-Based Multi-Agent Reinforcement Learning

Breakthrough the Suboptimal Stable Point in Value-Factorization-Based Multi-Agent Reinforcement Learning explores A novel Multi-Round Value Factorization framework that breaks suboptimal stable points in multi-agent reinforcement learning by iteratively filtering inferior actions.. Commercial viability score: 7/10 in Multi-Agent Reinforcement Learning.