{"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/a-survey-of-deep-network-solutions-for","title":"A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation","arxiv_id":"1612.07139","date":"2016-12-21","proceeding":null,"authors":["Lei Tai","Jingwei Zhang","Ming Liu","Joschka Boedecker","Wolfram Burgard"],"abstract":"Deep learning techniques have been widely applied, achieving state-of-the-art\nresults in various fields of study. This survey focuses on deep learning\nsolutions that target learning control policies for robotics applications. We\ncarry out our discussions on the two main paradigms for learning control with\ndeep networks: deep reinforcement learning and imitation learning. For deep\nreinforcement learning (DRL), we begin from traditional reinforcement learning\nalgorithms, showing how they are extended to the deep context and effective\nmechanisms that could be added on top of the DRL algorithms. We then introduce\nrepresentative works that utilize DRL to solve navigation and manipulation\ntasks in robotics. We continue our discussion on methods addressing the\nchallenge of the reality gap for transferring DRL policies trained in\nsimulation to real-world scenarios, and summarize robotics simulation platforms\nfor conducting DRL research. For imitation leaning, we go through its three\nmain categories, behavior cloning, inverse reinforcement learning and\ngenerative adversarial imitation learning, by introducing their formulations\nand their corresponding robotics applications. Finally, we discuss the open\nchallenges and research frontiers.","url_abs":"http://arxiv.org/abs/1612.07139v4","url_pdf":"http://arxiv.org/pdf/1612.07139v4.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":"a-survey-of-deep-network-solutions-for","repo_url":"https://github.com/tccnchsu/study","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":"imitation-learning","task_name":"Imitation 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":null,"atlas_url":"https://app.syntology.ai/?focus=1612.07139","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}