TSC：基于拓扑条件的Stackelberg协调用于交互驾驶中的多智能体强化学习
TSC: Topology-Conditioned Stackelberg Coordination for Multi-Agent Reinforcement Learning in Interactive Driving
AI总结：
TSC通过拓扑条件的Stackelberg协调方法，在无通信执行下实现密集交通中的去中心化多智能体强化学习，提升训练稳定性和安全性。
摘要：
在密集交通中实现安全高效的自动驾驶本质上是一个去中心化的多智能体协调问题，其中在冲突点如合并和穿插处的交互必须在部分可观测性下可靠解决。仅凭局部和不完整的线索，交互模式可以迅速变化，常常导致不稳定的行为，如振荡让行或不安全的承诺。现有的多智能体强化学习（MARL）方法要么采用同步决策，加剧了非平稳性，要么依赖于集中式排序机制，随着交通密度增加而扩展性差。为了解决这些限制，我们提出了拓扑条件的Stackelberg协调（TSC），一种在无通信执行下的去中心化交互驾驶学习框架，从受编织启发的轨迹穿插关系中提取出时间变化的有向优先图，从而在不构建全局顺序的情况下定义本地的领导者-追随者依赖关系。基于此图，TSC内生地将密集交互分解为图本地的Stackelberg子游戏，并在集中训练和去中心化执行（CTDE）下学习一个顺序协调策略，通过动作预测预判领导者，通过动作条件的值学习训练追随者以近似局部最优反应，从而在密集交通中提高训练稳定性和安全性。在四个密集交通场景中的实验表明，TSC在关键指标上优于代表性的MARL基线，特别是在减少碰撞的同时保持竞争性的交通效率和控制平滑性。
英文摘要：
Safe and efficient autonomous driving in dense traffic is fundamentally a decentralized multi-agent coordination problem, where interactions at conflict points such as merging and weaving must be resolved reliably under partial observability. With only local and incomplete cues, interaction patterns can change rapidly, often causing unstable behaviors such as oscillatory yielding or unsafe commitments. Existing multi-agent reinforcement learning (MARL) approaches either adopt synchronous decision-making, which exacerbate non-stationarity, or depend on centralized sequencing mechanisms that scale poorly as traffic density increases. To address these limitations, we propose Topology-conditioned Stackelberg Coordination (TSC), a learning framework for decentralized interactive driving under communication-free execution, which extracts a time-varying directed priority graph from braid-inspired weaving relations between trajectories, thereby defining local leader-follower dependencies without constructing a global order of play. Conditioned on this graph, TSC endogenously factorizes dense interactions into graph-local Stackelberg subgames and, under centralized training and decentralized execution (CTDE), learns a sequential coordination policy that anticipates leaders via action prediction and trains followers through action-conditioned value learning to approximate local best responses, improving training stability and safety in dense traffic. Experiments across four dense traffic scenarios show that TSC achieves superior performance over representative MARL baselines across key metrics, most notably reducing collisions while maintaining competitive traffic efficiency and control smoothness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
