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DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay
Abstract
In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.
Keywords
Deep reinforcement learning, Traffic light control, Dual experience replay, Dynamic epoch function
Citation Format:
Zhichao Yang, Yan Kong, Chih-Hsien Hsia, "DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay," Journal of Internet Technology, vol. 25, no. 1 , pp. 79-86, Jan. 2024.
Zhichao Yang, Yan Kong, Chih-Hsien Hsia, "DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay," Journal of Internet Technology, vol. 25, no. 1 , pp. 79-86, Jan. 2024.
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Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
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