{"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/deeptraffic-crowdsourced-hyperparameter","title":"DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation","arxiv_id":"1801.02805","date":"2018-01-09","proceeding":null,"authors":["Lex Fridman","Jack Terwilliger","Benedikt Jenik"],"abstract":"We present a traffic simulation named DeepTraffic where the planning systems\nfor a subset of the vehicles are handled by a neural network as part of a\nmodel-free, off-policy reinforcement learning process. The primary goal of\nDeepTraffic is to make the hands-on study of deep reinforcement learning\naccessible to thousands of students, educators, and researchers in order to\ninspire and fuel the exploration and evaluation of deep Q-learning network\nvariants and hyperparameter configurations through large-scale, open\ncompetition. This paper investigates the crowd-sourced hyperparameter tuning of\nthe policy network that resulted from the first iteration of the DeepTraffic\ncompetition where thousands of participants actively searched through the\nhyperparameter space.","url_abs":"http://arxiv.org/abs/1801.02805v2","url_pdf":"http://arxiv.org/pdf/1801.02805v2.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":"deeptraffic-crowdsourced-hyperparameter","repo_url":"https://github.com/Bhaney44/MIT_DeepTraffic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deeptraffic-crowdsourced-hyperparameter","repo_url":"https://github.com/NeekhilD/Learning-Competition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deeptraffic-crowdsourced-hyperparameter","repo_url":"https://github.com/asarav/MIT-Deep-Traffic-Solution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deeptraffic-crowdsourced-hyperparameter","repo_url":"https://github.com/ashtawy/deeptraffic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deeptraffic-crowdsourced-hyperparameter","repo_url":"https://github.com/lexfridman/deeptraffic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deeptraffic-crowdsourced-hyperparameter","repo_url":"https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P7-Highway-Driving","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"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":[{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}