Back to Basics: Deep Reinforcement Learning in Traffic Signal Control
This work addresses urban traffic flow optimization for transportation systems, representing an incremental improvement over existing RL methods.
The paper tackled traffic signal control by proposing RLight, a deep reinforcement learning method that improved performance and sped up learning by 30% through state representation and MDP reformulation, outperforming state-of-the-art algorithms on the Hangzhou traffic dataset.
In this paper we revisit some of the fundamental premises for a reinforcement learning (RL) approach to self-learning traffic lights. We propose RLight, a combination of choices that offers robust performance and good generalization to unseen traffic flows. In particular, our main contributions are threefold: our lightweight and cluster-aware state representation leads to improved performance; we reformulate the Markov Decision Process (MDP) such that it skips redundant timesteps of yellow light, speeding up learning by 30%; and we investigate the action space and provide insight into the difference in performance between acyclic and cyclic phase transitions. Additionally, we provide insights into the generalisation of the methods to unseen traffic. Evaluations using the real-world Hangzhou traffic dataset show that RLight outperforms state-of-the-art rule-based and deep reinforcement learning algorithms, demonstrating the potential of RL-based methods to improve urban traffic flows.