一篇新论文提出了MAPS架构，用层次化强化学习解决无信号交叉口的多车协调问题，在72种场景下实现无碰撞且更快通行，还能零样本从3车迁移到5车，效果很扎实。
针对多智能体强化学习（MARL）在无信号交叉口面临的组合动作空间和特权信息依赖问题，研究者提出Master-Agent Proto-plan System（MAPS）。该系统通过中央Master生成紧凑连续嵌入（proto-plan）编码全局策略，Worker基于局部观测和嵌入执行控制。在HighwayEnv的72种交叉口配置中，MAPS实现无碰撞导航并显著降低平均旅行时间。训练3个智能体的系统在零样本部署到5个智能体场景时成功率达94%，验证了proto-plan层次化学习的泛化能力。
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs. We propose Master-Agent Proto-plan System (MAPS), a hierarchical deep reinforcement learning (DRL) architecture in which a centralized Master agent generates a compact, continuous embedding, denoted as proto-plan, that encodes a global coordination strategy. Decentralized Worker agents integrate this embedding with local observations to execute vehicle-specific control, decoupling strategic intent from tactical execution and enabling independent optimization of each module. As a proof-of-concept evaluation of this coordination mechanism, we test MAPS across 72 intersection configurations in HighwayEnv. MAPS achieves collision-free navigation while significantly reducing average travel time, outperforming state-of-the-art baselines. The learned proto-plans further exhibit robust generalization: a system trained with three agents achieves a 94% success rate when deployed zero-shot to five-agent scenarios, confirming that proto-plan-based hierarchical learning provides a promising framework for multi-vehicle coordination.