{"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-an-overview","title":"Deep Reinforcement Learning: An Overview","arxiv_id":"1701.07274","date":"2017-01-25","proceeding":null,"authors":["Yuxi Li"],"abstract":"We give an overview of recent exciting achievements of deep reinforcement\nlearning (RL). We discuss six core elements, six important mechanisms, and\ntwelve applications. We start with background of machine learning, deep\nlearning and reinforcement learning. Next we discuss core RL elements,\nincluding value function, in particular, Deep Q-Network (DQN), policy, reward,\nmodel, planning, and exploration. After that, we discuss important mechanisms\nfor RL, including attention and memory, unsupervised learning, transfer\nlearning, multi-agent RL, hierarchical RL, and learning to learn. Then we\ndiscuss various applications of RL, including games, in particular, AlphaGo,\nrobotics, natural language processing, including dialogue systems, machine\ntranslation, and text generation, computer vision, neural architecture design,\nbusiness management, finance, healthcare, Industry 4.0, smart grid, intelligent\ntransportation systems, and computer systems. We mention topics not reviewed\nyet, and list a collection of RL resources. After presenting a brief summary,\nwe close with discussions.\n  Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant\nupdate.","url_abs":"http://arxiv.org/abs/1701.07274v6","url_pdf":"http://arxiv.org/pdf/1701.07274v6.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-an-overview","repo_url":"https://github.com/siemens/industrialbenchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deep-reinforcement-learning-an-overview","repo_url":"https://github.com/ADGEfficiency/dsr_rl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"management","task_name":"Management"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.07274","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}