[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-327330-105":59,"doc-detail-327330-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","full-bayesian-reinforcement-learning-via-lf-ibis","Full Bayesian Reinforcement Learning via LF-IBIS","","Reinforcement Learning models sequential decisions where an agent maximizes cumulative rewards through interaction. Bayesian Reinforcement Learning improves data efficiency by combining prior knowledge with sequential belief updates, yet many methods depend on an explicit likelihood that is often unavailable or intractable. LF-IBIS updates beliefs online for full Bayesian inference by combining Approximate Bayesian Computation and Iterated Batch Importance Sampling, yielding posterior distributions over environment parameters and optimal policies. Experiments include simulation validation in response-adaptive randomization for clinical trials and online policy updating when closed forms are unavailable.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/full-bayesian-reinforcement-learning-via-lf-ibis/327330/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/full-bayesian-reinforcement-learning-via-lf-ibis/327330.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does LF-IBIS address in Bayesian Reinforcement Learning?","Question",{"text":112,"@type":113},"It addresses the common requirement for an explicit likelihood in Bayesian Reinforcement Learning, which is frequently inaccessible or intractable in real-world settings.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does LF-IBIS perform inference without an explicit likelihood?",{"text":117,"@type":113},"LF-IBIS combines Approximate Bayesian Computation with Iterated Batch Importance Sampling to update beliefs online using comparisons between observed and simulated data.",{"name":119,"@type":110,"acceptedAnswer":120},"What outputs does the proposed method produce for decision-making?",{"text":121,"@type":113},"It yields approximate posterior distributions over both environment parameters and optimal policies, enabling uncertainty quantification for the exploration–exploitation trade-off.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},327330,1791095867,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","arXiv :2607 .01741v1 [ stat .ML] 2 Jul 2026  \nFull Bayesian Reinforcement Learning via LF-IBIS Stefano Masini∗ Cecilia Viscardi† Michela Baccini‡  \nAbstract  \nReinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by maximizing cumulative rewards. Among RL methods, Bayesian Reinforcement Learning (BRL) addresses common practical challenges related to data scarcity by leveraging prior knowledge about the environment and sequential belief updates. However, most BRL approaches require an explicit likelihood function, which is frequently inaccessible or intractable in real-world settings. We propose LikelihoodFree Iterated Batch Importance Sampling (LF-IBIS), a novel algorithm for BRL that updates the agent’s beliefs online as new interactions become available. By combining Approximate Bayesian Computation with Iterated Batch Importance Sampling, LF-IBIS enables full Bayesian inference also in settings where the environment dynamics is not described by an explicit or tractable likelihood. The method yields approximate posterior distributions over both environment parameters and optimal policies, providing a quantification of policy uncertainty useful fora Bayesian treatment of the exploration–exploitation trade-off. We test the method on a simulation study in response-adaptive randomization in clinical trials, where closed-form posteriors enable validation. Additional experiments address settings where the posterior has no closed form and illustrate online policy updating based on the posterior distribution of the optimal policy.  \nKeywords: Likelihood-free inference, Importance Sampling, Bayesian Reinforcement Learning, Sequential Learning, Response-Adaptive Randomization  \n1 Introduction  \nReinforcement Learning (RL) is a computational framework for sequential decision-making in which an agent interacts with an environment to learn how to choose actions that maximize cumulative rewards over time [33] . At each step, the agent observes the current state, selects an action, and receives a reward or penalty along with a successor state, gradually improving its behaviour through trial and error. RL has become widely used in several domains—including robotics, healthcare and online decision systems—thanks to its ability to autonomously discover effective strategies in complex and uncertain settings. RL methods are commonly classified into model-free and model-based approaches.  \nModel-based RL learns a model of the environment behaviour —often formalized as a  \n∗ Department of Computer Science, University of Pisa, Italy. Email:stefano .masini@phd.unipi .it †Department of Economics and Statistics, University of Salerno, Italy. Email: cviscardi@unisa .it ‡Department of Statistics, Computer Science, Applications ”G.Parenti”, University of Florence, Italy.  \nEmail: [michela.baccini@unifi.it](michela.baccini@unifi.it)  \nMarkov Decision Process (MDP)—that specifies the state-transition dynamics [39] . The agent learns this model from interaction data and uses it to plan and compute its policies. In contrast, model-free methods learn policies (or value functions) directly from observed state–action–reward sequences. In this work, we focus on the model-based formulation.  \nIn classical RL, effective learning usually requires a large amount of data collected through many interactions with the environment [33] . In many real-world applications, however, acquiring such interaction data is expensive, limited, or risky. This exacerbates the well-known exploration–exploitation trade-off: the choice between exploring new actions and gather better information, or exploiting actions currently believed to be optimal to maximize immediate rewards [33, 1] . Bayesian Reinforcement Learning (BRL) addresses this challenge by integrating the principles of Bayesian inference with RL, enabling the incorporation of prior knowledge and a continual updating of the be","cbCailnafrENEAyY","https://ap.wps.com/l/cbCailnafrENEAyY","pdf",2044086,37,"English","# Abstract\n# 1 Introduction","[{\"question\":\"What problem does LF-IBIS address in Bayesian Reinforcement Learning?\",\"answer\":\"It addresses the common requirement for an explicit likelihood in Bayesian Reinforcement Learning, which is frequently inaccessible or intractable in real-world settings.\"},{\"question\":\"How does LF-IBIS perform inference without an explicit likelihood?\",\"answer\":\"LF-IBIS combines Approximate Bayesian Computation with Iterated Batch Importance Sampling to update beliefs online using comparisons between observed and simulated data.\"},{\"question\":\"What outputs does the proposed method produce for decision-making?\",\"answer\":\"It yields approximate posterior distributions over both environment parameters and optimal policies, enabling uncertainty quantification for the exploration–exploitation trade-off.\"}]","Full Bayesian Reinforcement Learning via LF-IBIS | PDF",1789976994,93]