Bayesian RL

RL: Reinforcement Learning

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Overview

Bayesian reinforcement learning studies how agents can learn and act when rewards, dynamics, or task goals are uncertain. Common approaches include posterior sampling such as Thompson sampling, Bayes-adaptive planning over beliefs, and Gaussian-process or variational models that use uncertainty to direct exploration. Methods are applied to tabular and linear Markov decision processes, partially observed tasks, continuous control, robotics, and offline learning, with regret guarantees and evaluations on control and manipulation benchmarks. A central tension is balancing informative exploration and safe, robust behavior while keeping inference tractable and avoiding misleading beliefs when the model is misspecified.

Latest papers

CardsList

34 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.0% of all new papers.

  1. Meta-RL with Bayesian Linear Task Models

    Dec 24, 2025Jingyang You, Hanna KurniawatiRepresentation LearningReinforcement Learning

    School of Computing Australian National University

  2. Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments

    Jul 24, 2025Gilberto Cunha, Alexandra Ramôa, André Sequeira +2Partially Observable RLBayesian RL

    High-Assurance Software Laboratory (HASLab), INESC TEC, Portugal · Department of Computer Science, University of Minho, Portugal · International Iberian Nanotechnology Laboratory (INL), Portugal

  3. Fully Offline Reinforcement Learning

    May 28, 2025Mattie Fellows, Clarisse Wibault, Uljad Berdica +3Bayesian RLOffline RL

    Foerster Lab for AI Research (FLAIR), Department of Engineering Science, University of Oxford · Machine Learning Research Group, Department of Engineering Science, University of Oxford

  4. Thompson Sampling for Infinite-Horizon Discounted Decision Processes

    May 14, 2024Daniel Adelman, Cagla Keceli, Alba V. Olivares-NadalThompson SamplingRegret Minimization in RL

    Booth School of Business, The University of Chicago, Chicago, IL 60637, USA · United Airlines, Chicago, IL 60605, USA · UNSW Business School, UNSW, Kensington NSW 2033, Australia

  5. Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation

    Apr 2, 2024Carlos Plou, Ana C. Murillo, Ruben Martinez-CantinModel-Based RLData-Efficient Robot Learning

    All authors are with the Instituto de Investigación en Ingeniería de Aragón (i3A) and DIIS, University of Zaragoza