MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning

Researchers propose MO-MIX, a new deep reinforcement learning approach that addresses multi-objective multi-agent cooperative decision-making problems. The method combines centralized training with decentralized execution and demonstrates superior performance over baseline methods while requiring less computational cost.