让多智能体学会互相配合完成多目标任务
本研究提出了偏好协调多智能体策略优化（PCMA），用于解决合作多目标多智能体强化学习中的冲突问题。PCMA为每个智能体学习协调的个性化偏好，使智能体在多个目标（如效率与公平）之间形成互补性权衡。理论证明，在一定条件下，偏好多样性可通过一阶改进分解推动团队整体提升。在多个合作多目标环境及实际交通控制场景中，PCMA同时提升了任务性能和权衡协调能力。
Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning
Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives. In this setting, conflicts arise not only across objectives but also across agents with different observations, roles, and contributions. We propose Preference Coordinated Multi-agent Policy Optimization (PCMA), which learns coordinated agent-specific preferences to enable complementary trade-offs among agents. Theoretically, we formulate cooperative MOMARL as a team-optimal game and show that, under suitable conditions, preference diversity can induce team improvement through a first-order improvement decomposition. Experiments on multiple cooperative MOMA environments and a practical traffic-control scenario show that PCMA improves both performance and trade-off coordination.