{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:27:39Z","timestamp":1773113259940,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging.  Existing approaches only enable the communication in a heuristic manner without considering the content\/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm.  Experimental results on real datasets demonstrate that UniComm universally improves the performance of existing state-of-the-art methods, and UniLight significantly outperforms existing methods on a wide range of traffic situations. Source codes are available at https:\/\/github.com\/zyr17\/UniLight.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/535","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"3854-3860","source":"Crossref","is-referenced-by-count":21,"title":["Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method"],"prefix":"10.24963","author":[{"given":"Qize","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University"},{"name":"Shanghai Key Laboratory of Data Science, Fudan University"},{"name":"Shanghai Institute of Intelligent Electronics & Systems"}]},{"given":"Minhao","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University"},{"name":"Shanghai Key Laboratory of Data Science, Fudan University"},{"name":"Shanghai Institute of Intelligent Electronics & Systems"}]},{"given":"Shengmin","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University"},{"name":"Shanghai Key Laboratory of Data Science, Fudan University"},{"name":"Shanghai Institute of Intelligent Electronics & Systems"}]},{"given":"Weiwei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University"},{"name":"Shanghai Key Laboratory of Data Science, Fudan University"},{"name":"Shanghai Institute of Intelligent Electronics & Systems"}]},{"given":"Baihua","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:10:14Z","timestamp":1658142614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/535"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/535","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}