Control of traffic light timing using decentralized deep reinforcement learning⁎This work was supported by Mitsubishi Electric Research Laboratories. At the time of submission, H. Maske was an intern at Mitsubishi Electric Research Laboratories; he is now with Ford Motor Company. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Maske, Harshal; Chu, Tianshu; Kalabić, Uroš
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 14936
• Page End: 14941
• Publication Date: 2020
• Subjects: Intelligent Transportation -- traffic control systems -- Automatic control -- optimization -- real-time operations in transportation -- reinforcement learning control -- integrated traffic management; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1980
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this work, we introduce a scalable, decentralized deep reinforcement learning (DRL) scheme for controlling traffic signalization. The work builds on previous results using multi-agent DRL, with a new state representation and reward definitions. The state representation is a coarse image of traffic and the definitions of reward functions are tested based on the simulated Monaco SUMO Traffic (MoST) scenario. Based on extensive numerical experimentation, we have found the most appropriate choice of the reward function is related to minimizing the average amount of time vehicles spent in the network, but with various modifications that improve the learning process. The resulting algorithm performs better than the previous one on which it is based and markedly better than a non-learning based, greedy policy.