ModelLight：基于模型的交通信号控制的元强化学习

交通信号控制对于交通基础设施的有效利用至关重要。车辆流量的快速增长和交通模式的变化使交通信号控制变得越来越有挑战性。基于强化学习（RL）的算法在处理交通信号控制方面已经显示出其潜力。然而，大多数现有的解决方案需要大量的训练数据，这对于许多现实世界的场景是不可接受的。本文提出了一个新的基于模型的元强化学习框架（ModelLight），用于交通信号控制。在ModelLight中，道路交叉口的集合模型和基于优化的元学习方法被用来提高基于RL的交通灯控制方法的数据效率。在真实世界的数据集上的实验表明，ModelLight的性能优于最先进的交通灯控制算法，同时大大减少了与真实世界环境互动的次数。

原文题目：ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control

原文：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.