高维状态+高效强化学习，让交通信号灯平均等待时间减少17.9%。
Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
- 用高维车辆状态信息建模，提升信号控制决策能力。
- 实测平均等待时间降低最高达17.9%，优于传统低维方法。
- 结合轻量化模型压缩，适合实际部署于车路协同系统。
基于强化学习的交通信号控制（TSC）依赖路口车辆信息构建状态表示，当前状态可为高维或多变量，也可为低维向量。现有研究认为高维状态对性能无显著提升，但我们通过实验表明，采用高维状态表示可使平均等待时间最多降低17.9%。该高维状态可通过成本较低的车路协同（V2I）通信实现，具备推广价值。此外，针对高维状态带来的计算开销，我们探索了模型剪枝等压缩技术，以提升推理效率，支持实时应用。
原文摘要 · Abstract (English)
In reinforcement learning-based (RL-based) traffic signal control (TSC), decisions on the signal timing are made based on the available information on vehicles at a road intersection. This forms the state representation for the RL environment which can either be high-dimensional containing several variables or a low-dimensional vector. Current studies suggest that using high dimensional state representations does not lead to improved performance on TSC. However, we argue, with experimental results, that the use of high dimensional state representations can, in fact, lead to improved TSC performance with improvements up to 17.9% of the average waiting time. This high-dimensional representation is obtainable using the cost-effective vehicle-to-infrastructure (V2I) communication, encouraging its adoption for TSC. Additionally, given the large size of the state, we identified the need to have computational efficient models and explored model compression via pruning.
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