{"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/an-efficient-deep-reinforcement-learning","title":"An Efficient Deep Reinforcement Learning Model for Urban Traffic Control","arxiv_id":"1808.01876","date":"2018-08-06","proceeding":null,"authors":["Yilun Lin","Xingyuan Dai","Li Li","Fei-Yue Wang"],"abstract":"Urban Traffic Control (UTC) plays an essential role in Intelligent\nTransportation System (ITS) but remains difficult. Since model-based UTC\nmethods may not accurately describe the complex nature of traffic dynamics in\nall situations, model-free data-driven UTC methods, especially reinforcement\nlearning (RL) based UTC methods, received increasing interests in the last\ndecade. However, existing DL approaches did not propose an efficient algorithm\nto solve the complicated multiple intersections control problems whose\nstate-action spaces are vast. To solve this problem, we propose a Deep\nReinforcement Learning (DRL) algorithm that combines several tricks to master\nan appropriate control strategy within an acceptable time. This new algorithm\nrelaxes the fixed traffic demand pattern assumption and reduces human invention\nin parameter tuning. Simulation experiments have shown that our method\noutperforms traditional rule-based approaches and has the potential to handle\nmore complex traffic problems in the real world.","url_abs":"http://arxiv.org/abs/1808.01876v2","url_pdf":"http://arxiv.org/pdf/1808.01876v2.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":"an-efficient-deep-reinforcement-learning","repo_url":"https://github.com/ZD1wang/Traffiti","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement 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":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}