Partially Observable RL

RL: Reinforcement Learning

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Overview

Partially observable reinforcement learning studies how an agent can make good decisions when it sees only incomplete or noisy clues about the world, as in the POMDP setting. Common approaches estimate a belief over hidden states, plan with that belief or a learned world model, or use recurrent networks and transformers to summarize observation and action histories; some agents also choose actions to gather information. These methods are used in settings such as visual navigation, robotics, medical control, and multi-agent tasks where teammates’ or opponents’ states are hidden. A central tension is how to retain the right information over long histories while keeping learning and planning reliable: memory can help, but inaccurate beliefs, critic errors, and weak evaluation can still limit performance.

Latest papers

CardsList

120 papers20 papers in the last four weeks, up 67% on the four weeks before. 0.1% of all new papers.

  1. Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation

    May 4, 2026Sofianos Panagiotis Fotias, Vassilis GaganisRL ControlLatent Dynamics Modeling

    School of Mining and Metallurgical Engineering, National Technical University of Athens, Athens 157 73, Greece · Institute of Geoenergy, Foundation for Research and Technology, Chania, 73100, Greece

  2. Forager: a lightweight testbed for continual learning with partial observability in RL

    May 1, 2026Steven Tang, Xinze Xiong, Anna Hakhverdyan +7RL BenchmarksLoss of Plasticity

    Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · CIFAR AI Chair

  3. Active World-Model with 4D-informed Retrieval for Exploration and Awareness

    Apr 17, 2026Elaheh Vaezpour, Amirhosein Javadi, Tara JavidiActive PerceptionWorld Model Learning

    KavAI · UCSD

  4. InfoChess: A Game of Adversarial Inference and a Laboratory for Quantifiable Information Control

    Apr 15, 2026Kieran A. MurphyImperfect-Information GamesGame-Playing Agents

    New Jersey Institute of Technology, Newark, NJ, United States

  5. Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication

    Apr 10, 2026Benjamin Amoh, Geoffrey Parker, Wesley MarreroMulti-Agent CoordinationMulti-Agent Communication

    Thayer School of Engineering, Dartmouth College

  6. PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC

    Apr 9, 2026Mohsen Amiri, Mohsen Amiri, Ali Beikmohammadi +2Partially Observable RLLearning Using Privileged Information

    The authors are with the Department of Computer and System Science, Stockholm University, 11419 Stockholm, Sweden · The authors are with the School of Mechanical and Material Engineering, Washington State University, Pullman, WA 99164, USA

  7. Belief-State RWKV for Reinforcement Learning under Partial Observability

    Apr 1, 2026Liu XiaoReinforcement LearningPartially Observable RL

  8. Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning

    Mar 28, 2026Naveen MysoreReinforcement LearningNon-Markovian RL

    Department of Electrical and Computer Engineering, University of California, Santa Barbara, USA

  9. Factorization Regret mediates compositional generalization in latent space

    Mar 28, 2026John SchwarczGeneralization in RLCompositional Representation Learning

    Edmond & Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem

  10. Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

    Mar 26, 2026João Castelo-Branco, José Santos-Victor, Alexandre BernardinoObject-Goal NavigationRobot Navigation

    Instituto Superior Técnico, Lisbon, Portugal · Institute For Systems and Robotics, Lisbon, Portugal

  11. Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning

    Mar 24, 2026Chao Han, Stefanos Ioannou, Luca Manneschi +4Model-Based RLLatent Dynamics Modeling

    School of Computer Science, The University of Sheffield, UK · Cancer Research UK National Biomarker Centre, The University of Manchester, UK · School of Chemical, Materials and Biological Engineering, The University of Sheffield, UK +2

  12. Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning

    Mar 24, 2026Chenghao Huang, Jiarong Fan, Weiqing Wang +1Power SystemsDecentralized MARL

    Department of Data Science and AI, Faculty of IT, Monash University, Australia · Monash Energy Institute, Monash University, Australia

  13. NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing

    Mar 20, 2026Raphael Simon, José Carrasquel, Elli Makdis Antoun +2RL BenchmarksPartially Observable RL

    CISS Department, Royal Military Academy, Belgium · AI Lab, Vrije Universiteit Brussel, Belgium

  14. Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment

    Mar 17, 2026Enguang Fan, Yifan Chen, Zihan Shan +3Cooperative MARLDecentralized MARL

    University of Illinois at Urbana-Champaign · Boeing Research and Technology

  15. STAIRS-Former: Spatio-Temporal Attention with Interleaved Recursive Structure Transformer for Offline Multi-task Multi-agent Reinforcement Learning

    Mar 12, 2026Jiwon Jeon, Myungsik Cho, Youngchul SungOffline MARLTransformer-Based RL

    School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, Republic of Korea

  16. UAV-MARL: Multi-Agent Reinforcement Learning for Time-Critical and Dynamic Medical Supply Delivery

    Mar 11, 2026Islam Guven, Mehmet ParlakMulti-Agent CoordinationMulti-Agent Reinforcement Learning

    ICTEAM, Université catholique de Louvain Ottignies-Louvain-la-Neuve, Belgium

  17. Adversarial Latent-State Training for Robust Policies in Partially Observable Domains

    Mar 7, 2026Angad Singh AhujaRobust RLPartially Observable RL

    Constrained Image-Synthesis Lab

  18. SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning

    Mar 5, 2026Manav Vora, Gokul Puthumanaillam, Hiroyasu Tsukamoto +1Multi-Agent CommunicationMulti-Agent Reinforcement Learning

    University of Illinois Urbana-Champaign

  19. When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift

    Mar 4, 2026Kevin Vogt-Lowell, Theodoros Tsiligkaridis, Rodney Lafuente-Mercado +4Robust RLPartially Observable RL

    MIT Lincoln Laboratory · Harvard · MIT

  20. Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling

    Mar 4, 2026Emile Anand, Ishani KarmarkarMean-Field MARLReinforcement Learning

    Georgia Institute of Technology, Atlanta, GA · Stanford University, Palo Alto, CA

  21. Next Embedding Prediction Makes World Models Stronger

    Mar 3, 2026George Bredis, Nikita Balagansky, Daniil Gavrilov +1Model-Based RLPartially Observable RL

    T-Tech.

  22. Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat

    Mar 1, 2026Jiahao Fu, Feng YangReinforcement LearningPartially Observable RL

    National Elite Institute of Engineering,Northwestern Polytechnical University, Xi’an, Shanxi, China · School of Automation, Northwestern Polytechnical University, Xi’an, Shanxi, China

  23. On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation

    Feb 24, 2026Alexander GalozyReinforcement LearningPartially Observable RL

    Center for Applied Intelligent Systems Research, Halmstad University, Sweden

  24. Recurrent Structural Policy Gradient for Partially Observable Mean Field Games

    Feb 23, 2026Clarisse Wibault, Johannes Forkel, Sebastian Towers +9Mean Field GamesPolicy Gradient Methods

    FLAIR, University of Oxford · MLRG, University of Oxford · ifo Institute, LMU Munich +5

  25. In-Context Planning with Latent Temporal Abstractions

    Feb 21, 2026Baiting Luo, Yunuo Zhang, Nathaniel S. Keplinger +3Model-Based PlanningOffline RL

    Vanderbilt University · William & Mary

  26. Model-Free Output Feedback Stabilization via Policy Gradient Methods

    Jan 27, 2026Ankang Zhang, Ming Chi, Xiaoling Wang +1Dynamical SystemsPartially Observable RL

    School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China · College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

  27. To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning

    Oct 3, 2025Yuda Song, Dhruv Rohatgi, Aarti Singh +1Partially Observable RLLearning Using Privileged Information

    Carnegie Mellon University · Massachusetts Institute of Technology · Aurora Innovation

  28. Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation

    Sep 24, 2025Raphael Simon, Pieter Libin, Wim MeesReinforcement LearningPartially Observable RL

    Cyber Defence Lab, CISS Department Royal Military Academy · AI Lab, Department of Computer Science Vrije Universiteit Brussel

  29. 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

  30. SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems

    Feb 6, 2024Oubo Ma, Yuwen Pu, Linkang Du +5Adversarial AttacksPartially Observable RL

    Zhejiang University · Xi’an Jiaotong University · Laboratory for Big Data and Decision +2