{"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/implications-of-decentralized-q-learning","title":"Implications of Decentralized Q-learning Resource Allocation in Wireless Networks","arxiv_id":"1705.10508","date":"2017-05-30","proceeding":null,"authors":["Francesc Wilhelmi","Boris Bellalta","Cristina Cano","Anders Jonsson"],"abstract":"Reinforcement Learning is gaining attention by the wireless networking\ncommunity due to its potential to learn good-performing configurations only\nfrom the observed results. In this work we propose a stateless variation of\nQ-learning, which we apply to exploit spatial reuse in a wireless network. In\nparticular, we allow networks to modify both their transmission power and the\nchannel used solely based on the experienced throughput. We concentrate in a\ncompletely decentralized scenario in which no information about neighbouring\nnodes is available to the learners. Our results show that although the\nalgorithm is able to find the best-performing actions to enhance aggregate\nthroughput, there is high variability in the throughput experienced by the\nindividual networks. We identify the cause of this variability as the\nadversarial setting of our setup, in which the most played actions provide\nintermittent good/poor performance depending on the neighbouring decisions. We\nalso evaluate the effect of the intrinsic learning parameters of the algorithm\non this variability.","url_abs":"http://arxiv.org/abs/1705.10508v2","url_pdf":"http://arxiv.org/pdf/1705.10508v2.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":"implications-of-decentralized-q-learning","repo_url":"https://github.com/wn-upf/Decentralized_Qlearning_Resource_Allocation_in_WNs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","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}