Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning

In the real world, most decisions involve multiple competing goals — reduce emissions and minimize congestion and maximize throughput — and multiple agents who must coordinate to achieve them. Existing multi-agent reinforcement learning often collapses these tensions into a single objective, losing important nuance. PCMA introduces the idea of letting agents develop their own specialized preferences, which together produce better team-level trade-offs. The authors ground this in solid game theory and test it on traffic control scenarios. Applications range from smart city traffic management and logistics coordination to robot swarms and multi-stakeholder resource allocation where no single agent has the full picture. Authors: Pengxin Wang, Lihao Guo, Yi Xie, Bo Liu, Siyang Cao, Jingdi Chen Paper: https://arxiv.org/abs/2606.14693v1