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.

本研究针对当前基于强化学习的交通信号控制（TSC）存在的高维状态表示未能有效提升性能的问题，提出了一种新的方法，证明高维状态表示可以显著提高TSC性能，平均等待时间可减少17.9%。此外，研究强调了通过使用成本效益高的车与基础设施（V2I）通信来实现高维状态表示的必要性，并探索了通过剪枝进行模型压缩以提高计算效率。

利用高维状态表示和高效深度强化学习优化交通信号控制