标题： 多残差专家混合学习在多车辆系统协同控制中的应用 显示英文标题

标题： Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems

摘要： 自动驾驶车辆（AVs）正变得越来越受欢迎，其应用已不再仅仅是交通方式，而是作为交通流的移动执行器，以控制流体动力学。 这与传统的固定位置执行器（如交通信号灯）形成对比，并被称为拉格朗日交通控制。 然而，设计一种能够在各种交通场景中泛化的有效拉格朗日交通控制策略是一个重大挑战。 现实世界的交通环境高度多样化，开发在这些多样化交通场景中表现稳健的策略是具有挑战性的。 此外，由于交通系统的多智能体特性、参与者之间的混合动机以及受严格物理和外部约束的冲突优化目标，问题变得更加复杂。 为了解决这些挑战，我们引入了多残差专家学习（MRMEL），这是一种新的拉格朗日交通控制框架，它通过学习一个残差来增强给定的次优基准策略，同时明确考虑交通场景空间的结构。 具体而言，受到残差强化学习的启发，MRMEL通过学习一个残差校正来增强次优基准AV控制策略，但同时根据交通场景动态地从一组条件性基准策略中选择最合适的基准策略，并将其建模为专家混合模型。 我们使用亚特兰大、达拉斯-沃思堡和盐湖城信号交叉口的协同生态驾驶案例研究来验证MRMEL，采用真实世界数据驱动的交通场景。 结果表明，MRMEL在每个设置中都能持续实现优于最强基线的性能，相对于每个设置中的最强基线，整体车辆排放量额外减少了4%-9%。 显示英文摘要

摘要： Autonomous vehicles (AVs) are becoming increasingly popular, with their applications now extending beyond just a mode of transportation to serving as mobile actuators of a traffic flow to control flow dynamics. This contrasts with traditional fixed-location actuators, such as traffic signals, and is referred to as Lagrangian traffic control. However, designing effective Lagrangian traffic control policies for AVs that generalize across traffic scenarios introduces a major challenge. Real-world traffic environments are highly diverse, and developing policies that perform robustly across such diverse traffic scenarios is challenging. It is further compounded by the joint complexity of the multi-agent nature of traffic systems, mixed motives among participants, and conflicting optimization objectives subject to strict physical and external constraints. To address these challenges, we introduce Multi-Residual Mixture of Expert Learning (MRMEL), a novel framework for Lagrangian traffic control that augments a given suboptimal nominal policy with a learned residual while explicitly accounting for the structure of the traffic scenario space. In particular, taking inspiration from residual reinforcement learning, MRMEL augments a suboptimal nominal AV control policy by learning a residual correction, but at the same time dynamically selects the most suitable nominal policy from a pool of nominal policies conditioned on the traffic scenarios and modeled as a mixture of experts. We validate MRMEL using a case study in cooperative eco-driving at signalized intersections in Atlanta, Dallas Fort Worth, and Salt Lake City, with real-world data-driven traffic scenarios. The results show that MRMEL consistently yields superior performance-achieving an additional 4%-9% reduction in aggregate vehicle emissions relative to the strongest baseline in each setting.