Imitation learning based deep reinforcement learning for traffic signal control

Cunxiao Qiu; Dake Zhou; Qingxian Wu; Tao Li

doi:10.1117/12.3004832

16 August 2023 Imitation learning based deep reinforcement learning for traffic signal control

Cunxiao Qiu, Dake Zhou, Qingxian Wu, Tao Li

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871F (2023) https://doi.org/10.1117/12.3004832
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

The traffic scenes are complex and ever-changing, and traditional signal control methods have limitations. DRL based methods rely on a large amount of interactive data between intelligent agents and the environment, which leads to a long learning process and high computational costs. Based on this, this paper introduces imitative learning into reinforcement learning for traffic signal control. At the same time, it improves the design of reward function for the problem of frequent phase switching and too long red-light in the flow imbalance scene, and increases the cost of phase switching and penalty factors. Experiments verify the effectiveness of the algorithm in this paper.

Citation Download Citation

Cunxiao Qiu, Dake Zhou, Qingxian Wu, and Tao Li "Imitation learning based deep reinforcement learning for traffic signal control", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871F (16 August 2023); https://doi.org/10.1117/12.3004832

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