Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
This addresses traffic congestion at intersections for urban planners and commuters, but it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of traffic signal control by demonstrating that high-dimensional state representations, contrary to prior studies, can improve performance, achieving up to a 17.9% reduction in average waiting time.
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.