{"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-radio-control-and","title":"Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent","arxiv_id":"1605.09221","date":"2016-05-30","proceeding":null,"authors":["Timothy J. O'Shea","T. Charles Clancy"],"abstract":"This paper presents research in progress investigating the viability and\nadaptation of reinforcement learning using deep neural network based function\napproximation for the task of radio control and signal detection in the\nwireless domain. We demonstrate a successful initial method for radio control\nwhich allows naive learning of search without the need for expert features,\nheuristics, or search strategies. We also introduce Kerlym, an open Keras based\nreinforcement learning agent collection for OpenAI's Gym.","url_abs":"http://arxiv.org/abs/1605.09221v1","url_pdf":"http://arxiv.org/pdf/1605.09221v1.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-radio-control-and","repo_url":"https://github.com/osh/kerlym","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}