{"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-framework-for","title":"Deep Reinforcement Learning framework for Autonomous Driving","arxiv_id":"1704.02532","date":"2017-04-08","proceeding":null,"authors":["Ahmad El Sallab","Mohammed Abdou","Etienne Perot","Senthil Yogamani"],"abstract":"Reinforcement learning is considered to be a strong AI paradigm which can be\nused to teach machines through interaction with the environment and learning\nfrom their mistakes. Despite its perceived utility, it has not yet been\nsuccessfully applied in automotive applications. Motivated by the successful\ndemonstrations of learning of Atari games and Go by Google DeepMind, we propose\na framework for autonomous driving using deep reinforcement learning. This is\nof particular relevance as it is difficult to pose autonomous driving as a\nsupervised learning problem due to strong interactions with the environment\nincluding other vehicles, pedestrians and roadworks. As it is a relatively new\narea of research for autonomous driving, we provide a short overview of deep\nreinforcement learning and then describe our proposed framework. It\nincorporates Recurrent Neural Networks for information integration, enabling\nthe car to handle partially observable scenarios. It also integrates the recent\nwork on attention models to focus on relevant information, thereby reducing the\ncomputational complexity for deployment on embedded hardware. The framework was\ntested in an open source 3D car racing simulator called TORCS. Our simulation\nresults demonstrate learning of autonomous maneuvering in a scenario of complex\nroad curvatures and simple interaction of other vehicles.","url_abs":"http://arxiv.org/abs/1704.02532v1","url_pdf":"http://arxiv.org/pdf/1704.02532v1.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-framework-for","repo_url":"https://github.com/gowriaddepalli/ML_DL_Research_collab_base","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"car-racing","task_name":"Car Racing"},{"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":"https://app.syntology.ai/?focus=1704.02532","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}