{"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/online-algorithms-for-pomdps-with-continuous","title":"Online algorithms for POMDPs with continuous state, action, and observation spaces","arxiv_id":"1709.06196","date":"2017-09-18","proceeding":null,"authors":["Zachary Sunberg","Mykel Kochenderfer"],"abstract":"Online solvers for partially observable Markov decision processes have been\napplied to problems with large discrete state spaces, but continuous state,\naction, and observation spaces remain a challenge. This paper begins by\ninvestigating double progressive widening (DPW) as a solution to this\nchallenge. However, we prove that this modification alone is not sufficient\nbecause the belief representations in the search tree collapse to a single\nparticle causing the algorithm to converge to a policy that is suboptimal\nregardless of the computation time. This paper proposes and evaluates two new\nalgorithms, POMCPOW and PFT-DPW, that overcome this deficiency by using\nweighted particle filtering. Simulation results show that these modifications\nallow the algorithms to be successful where previous approaches fail.","url_abs":"http://arxiv.org/abs/1709.06196v6","url_pdf":"http://arxiv.org/pdf/1709.06196v6.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":"online-algorithms-for-pomdps-with-continuous","repo_url":"https://github.com/zsunberg/ContinuousPOMDPTreeSearchExperiments.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"online-algorithms-for-pomdps-with-continuous","repo_url":"https://github.com/AdaCompNUS/magic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"online-algorithms-for-pomdps-with-continuous","repo_url":"https://github.com/JuliaPOMDP/POMCPOW.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"online-algorithms-for-pomdps-with-continuous","repo_url":"https://github.com/sisl/PA-POMCPOW.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.06196","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}