CenLight: Centralized traffic grid signal optimization via action and state decomposition

Abstract

The centralized traffic grid signal control by the reinforcement learning method is challenging due to the difficulties of searching policy in the large state and action space. In order to solve these problems, a deep reinforcement learning (DRL) method via the action and state decomposition mechanism is proposed. We apply long short-term memory to construct the agent which decomposes the high-dimensional state and action space into sub-spaces and makes decisions incrementally. This is a significant difference between our method and other methods. Through the specifically designed structure of the agent, the difficulty of searching policies can be mitigated, and our method can effectively control the traffic lights in a grid with hundreds of intersections. Experiments on synthetic data and real-world data show that our method has better performance than traditional control methods and state-of-the-art DRL-based methods with an improvement of 21% on the queue length on synthetic data and the best travel time with an improvement of 9.35% on real-world data.