{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-reinforcement-learning-for-traffic-light","title":"Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks","arxiv_id":"1803.11115","date":"2018-03-29","proceeding":null,"authors":["Xiaoyuan Liang","Xunsheng Du","Guiling Wang","Zhu Han"],"abstract":"Existing inefficient traffic light control causes numerous problems, such as\nlong delay and waste of energy. To improve efficiency, taking real-time traffic\ninformation as an input and dynamically adjusting the traffic light duration\naccordingly is a must. In terms of how to dynamically adjust traffic signals'\nduration, existing works either split the traffic signal into equal duration or\nextract limited traffic information from the real data. In this paper, we study\nhow to decide the traffic signals' duration based on the collected data from\ndifferent sensors and vehicular networks. We propose a deep reinforcement\nlearning model to control the traffic light. In the model, we quantify the\ncomplex traffic scenario as states by collecting data and dividing the whole\nintersection into small grids. The timing changes of a traffic light are the\nactions, which are modeled as a high-dimension Markov decision process. The\nreward is the cumulative waiting time difference between two cycles. To solve\nthe model, a convolutional neural network is employed to map the states to\nrewards. The proposed model is composed of several components to improve the\nperformance, such as dueling network, target network, double Q-learning\nnetwork, and prioritized experience replay. We evaluate our model via\nsimulation in the Simulation of Urban MObility (SUMO) in a vehicular network,\nand the simulation results show the efficiency of our model in controlling\ntraffic lights.","url_abs":"http://arxiv.org/abs/1803.11115v1","url_pdf":"http://arxiv.org/pdf/1803.11115v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-reinforcement-learning-for-traffic-light","repo_url":"https://github.com/Ring367/A-Deep-Reinforcement-Learning-Network-for-Traffic-Light-Cycle-Control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-traffic-light","repo_url":"https://github.com/Ring367/Double-Dueling-DQN-to-control-traffic-lights-on-SUMO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-traffic-light","repo_url":"https://github.com/kathyrnrouse/RL_CUIP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.11115","atlas_url":"https://app.syntology.ai/?focus=1803.11115","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}