ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control
This addresses data efficiency for traffic signal control in real-world scenarios, though it appears incremental as it builds on existing RL methods.
The paper tackles the problem of data inefficiency in reinforcement learning for traffic signal control by proposing ModelLight, a model-based meta-reinforcement learning framework, which outperforms state-of-the-art algorithms and reduces required real-world interactions.
Traffic signal control is of critical importance for the effective use of transportation infrastructures. The rapid increase of vehicle traffic and changes in traffic patterns make traffic signal control more and more challenging. Reinforcement Learning (RL)-based algorithms have demonstrated their potential in dealing with traffic signal control. However, most existing solutions require a large amount of training data, which is unacceptable for many real-world scenarios. This paper proposes a novel model-based meta-reinforcement learning framework (ModelLight) for traffic signal control. Within ModelLight, an ensemble of models for road intersections and the optimization-based meta-learning method are used to improve the data efficiency of an RL-based traffic light control method. Experiments on real-world datasets demonstrate that ModelLight can outperform state-of-the-art traffic light control algorithms while substantially reducing the number of required interactions with the real-world environment.