{"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/federated-control-with-hierarchical-multi","title":"Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning","arxiv_id":"1712.08266","date":"2017-12-22","proceeding":null,"authors":["Saurabh Kumar","Pararth Shah","Dilek Hakkani-Tur","Larry Heck"],"abstract":"We present a framework combining hierarchical and multi-agent deep\nreinforcement learning approaches to solve coordination problems among a\nmultitude of agents using a semi-decentralized model. The framework extends the\nmulti-agent learning setup by introducing a meta-controller that guides the\ncommunication between agent pairs, enabling agents to focus on communicating\nwith only one other agent at any step. This hierarchical decomposition of the\ntask allows for efficient exploration to learn policies that identify globally\noptimal solutions even as the number of collaborating agents increases. We show\npromising initial experimental results on a simulated distributed scheduling\nproblem.","url_abs":"http://arxiv.org/abs/1712.08266v1","url_pdf":"http://arxiv.org/pdf/1712.08266v1.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":"federated-control-with-hierarchical-multi","repo_url":"https://github.com/skumar9876/FCRL","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":"efficient-exploration","task_name":"Efficient Exploration"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}