提出JL-GAT方法，让多智能体交通信号控制更好适应真实路况。
Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
- 基于邻近智能体信息改进动作转换，增强多智能体策略的现实适应性
- 在模拟恶劣天气下多路网测试中，性能比基线提升12.3%以上
- 适合关注交通信号控制落地应用的研究者与工程团队
交通信号控制对管理城市交通流、缓解拥堵至关重要。强化学习（RL）通过响应动态交通模式提供自适应方案，多智能体强化学习（MARL）因路口天然具备协同代理特性而日益受到关注。然而，由于环境动态变化，基于MARL的交通信号控制策略在真实世界部署时常出现显著性能下降，即存在“仿真到现实”差距。已有地面动作转换（GAT）在单智能体场景中有效缓解了该问题，但真实交通网络包含大量交互路口，更适合采用多智能体框架。本文提出JL-GAT，将GAT应用于基于MARL的交通信号控制，通过引入邻近智能体信息，在保持可扩展性的同时增强策略的接地能力。JL-GAT采用去中心化设计，兼顾实际交通网络所需的可扩展性与关键智能体间交互捕捉能力。在多种道路网络及模拟恶劣天气条件下的全面实验，结合消融研究，验证了JL-GAT的有效性。代码已公开于https://github.com/DaRL-LibSignal/JL-GAT/。
原文摘要 · Abstract (English)
Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dynamic traffic patterns, with multi-agent RL (MARL) gaining traction as intersections naturally function as coordinated agents. However, due to shifts in environmental dynamics, implementing MARL-based TSC policies in the real world often leads to a significant performance drop, known as the sim-to-real gap. Grounded Action Transformation (GAT) has successfully mitigated this gap in single-agent RL for TSC, but real-world traffic networks, which involve numerous interacting intersections, are better suited to a MARL framework. In this work, we introduce JL-GAT, an application of GAT to MARL-based TSC that balances scalability with enhanced grounding capability by incorporating information from neighboring agents. JL-GAT adopts a decentralized approach to GAT, allowing for the scalability often required in real-world traffic networks while still capturing key interactions between agents. Comprehensive experiments on various road networks under simulated adverse weather conditions, along with ablation studies, demonstrate the effectiveness of JL-GAT. The code is publicly available at https://github.com/DaRL-LibSignal/JL-GAT/.
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