用路网OD对代替个体司机，提升交通分配的可扩展性与可靠性。
Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment
- 将代理定义为起点终点对路由器，大幅提高大规模网络的可扩展性。
- 10步内完成分配，相对差距比传统方法低94.99%。
- 适合城市级交通建模，尤其适用于高需求复杂路网场景。
都市化进程加速与出行需求增长对交通分配方法提出更高要求。多智能体强化学习（MARL）在无需显式系统动态的情况下建模自适应路径选择行为，优于传统方法，有利于实际部署。然而，现有MARL框架在处理大规模网络和高出行需求时面临可扩展性与可靠性挑战，限制其在大规模交通分配中的应用。为此，本文提出MARL-OD-DA框架，将智能体重新定义为起讫点（OD）对路由单元，显著提升可扩展性；设计基于狄利克雷分布的动作空间并引入动作剪枝策略，结合基于局部相对差距的奖励函数，增强解的可靠性与收敛效率。实验表明，该框架能有效处理具有多样性和高密度城市级OD需求的中等规模网络。在SiouxFalls网络上，仅用10步即获得更优分配结果，相对差距较传统方法降低94.99%。
原文摘要 · Abstract (English)
The evolution of metropolitan cities and the increase in travel demands impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) approaches outperform traditional methods in modeling adaptive routing behavior without requiring explicit system dynamics, which is beneficial for real-world deployment. However, MARL frameworks face challenges in scalability and reliability when managing extensive networks with substantial travel demand, which limiting their practical applicability in solving large-scale traffic assignment problems. To address these challenges, this study introduces MARL-OD-DA, a new MARL framework for the traffic assignment problem, which redefines agents as origin-destination (OD) pair routers rather than individual travelers, significantly enhancing scalability. Additionally, a Dirichlet-based action space with action pruning and a reward function based on the local relative gap are designed to enhance solution reliability and improve convergence efficiency. Experiments demonstrate that the proposed MARL framework effectively handles medium-sized networks with extensive and varied city-level OD demand, surpassing existing MARL methods. When implemented in the SiouxFalls network, MARL-OD-DA achieves better assignment solutions in 10 steps, with a relative gap that is 94.99% lower than that of conventional methods.
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