{"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/scalable-planning-and-learning-for-multiagent","title":"Scalable Planning and Learning for Multiagent POMDPs: Extended Version","arxiv_id":"1404.1140","date":"2014-04-04","proceeding":null,"authors":["Christopher Amato","Frans A. Oliehoek"],"abstract":"Online, sample-based planning algorithms for POMDPs have shown great promise\nin scaling to problems with large state spaces, but they become intractable for\nlarge action and observation spaces. This is particularly problematic in\nmultiagent POMDPs where the action and observation space grows exponentially\nwith the number of agents. To combat this intractability, we propose a novel\nscalable approach based on sample-based planning and factored value functions\nthat exploits structure present in many multiagent settings. This approach\napplies not only in the planning case, but also in the Bayesian reinforcement\nlearning setting. Experimental results show that we are able to provide high\nquality solutions to large multiagent planning and learning problems.","url_abs":"http://arxiv.org/abs/1404.1140v2","url_pdf":"http://arxiv.org/pdf/1404.1140v2.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":"scalable-planning-and-learning-for-multiagent","repo_url":"https://github.com/JuliaPOMDP/FactoredValueMCTS.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"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":"https://app.syntology.ai/?focus=1404.1140","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}