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

Abstract

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.

References in corpus (9)

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Model-Based Reinforcement Learning for Atari
• Benchmarking Model-Based Reinforcement Learning
• Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning
• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
• Model-Based Reinforcement Learning via Meta-Policy Optimization
• GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
• Learning to Paint With Model-based Deep Reinforcement Learning
• Diagnosing Reinforcement Learning for Traffic Signal Control

Cited by in corpus (1)

• Reinforcement Learning Approaches for Traffic Signal Control under Missing Data