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

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

  1. TANDEM: Balancing Centralized and Decentralized Critics to Address the Information Gap in Complex Multi-Agent Environments

    Oct 27, 2026Alireza Saleh Abadi, Leen-Kiat Soh, Adam Eck +1Cooperative MARLCentralized Training with Decentralized Execution

    School of Computing, University of Nebraska-Lincoln, Lincoln, Nebraska, USA · Department of Computer Science, Oberlin College, Oberlin, Ohio, USA · Institute for AI & School of Computing, University of Georgia, Athens, Georgia, USA

  2. Memory or a Clock? Auditing a Memory Advantage in XLand-MiniGrid with Seeds, Maps and Evaluation Noise Apart

    Oct 24, 2026Rodrigo Reveron-FonsecaReinforcement LearningMemory-Augmented RL

    Tecnológico de Monterrey

  3. Asymmetric On-Policy Distillation Learns In-Context Exploration

    Oct 21, 2026Sriyash Poddar, Yanda Bao, Jacob Krantz +5RL ExplorationPartially Observable RL

    Paul G. Allen School of Computer Science and Engineering, University of Washington · Work partially done while at Meta

  4. Interruption-Aware Recurrent Reinforcement Learning for UAS Navigation Under Temporary Overrides

    Oct 20, 2026Christopher Conrad, Deepak Panda, Weisi Guo +1UAV NavigationReinforcement Learning

    Centre for Assured and Connected Autonomy, Cranfield University, Bedford, UK

  5. From POMDP Theory to Deep RL with Particle Filters

    Oct 20, 2026Kevin Tan, Ziping XuPartially Observable RLModel-Based RL

    Wharton Statistics and Data Science, University of Pennsylvania · School of Data and Information Sciences, University of North Carolina at Chapel Hill

  6. Dynamics-Aligned Representation Learning under Induced Partial Observability

    Oct 18, 2026SM Mazharul Islam, Manfred HuberModel-Based RLRepresentation Learning for RL

    Department of Computer Science and Engineering, University of Texas at Arlington, TX-76019, USA

  7. Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty

    Oct 15, 2026Chengze Xie, Xuan Wang, Ying Ding +2Robust RLSequential Decision Making

    Department of Industrial and Systems Engineering, Texas A&M University. · Department of Mechanical Engineering, The University of Texas at Austin. · School of Information, The University of Texas at Austin.

  8. When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability

    Oct 13, 2026Marcel Petran, Stefan EdelkampOpponent ModelingMulti-Agent Reinforcement Learning

    Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

  9. Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning

    Oct 12, 2026Li-Ching Chen, Andrés Franco Valiente, Yuta Ishiyama +7Clinical Decision-MakingRL for Language Models

    University of California, San Francisco · Weill Cancer Hub West · Vokram Inc. +1

  10. Belief Embedding Tree Search

    Oct 10, 2026Christopher Solinas, Radovan Haluška, Martin Schmid +3Partially Observable RLBelief-Space Planning

    University of Alberta · Charles University, Prague · EquiLibre Technologies, Inc. +1

  11. Does Learning Recurrent Networks Matter in Partially Observable RL?

    Oct 9, 2026Zekang Wang, Zhe He, Gaojie JinPartially Observable RLPartially Observable Markov Decision Processes

    University of Macau Macau SAR, China

  12. Partially Observable Zero-shot coordination by Predicting Intention of Partner

    Oct 6, 2026Jinnyeong Yang, Yuhwan Jeong, Hoyong Kwon +3Multi-Agent CoordinationPartially Observable RL

    KAIST, Visual Intelligence Lab

  13. Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments

    Oct 5, 2026Youcef Mehamlia, Nadir Farhi, Meriem BoualiRL ControlDeep Q-Learning

    Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France. · Laboratoire LITAN, ESTIN, Amizour, Algeria.

  14. EpicWorldModel: Exploration-driven Planning with Latent World Models

    Oct 5, 2026Bowen Feng, Julian Ost, May Mei +2Partially Observable RLWorld Model-Based Planning

    Princeton University · Torc Robotics

  15. When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game

    Oct 2, 2026Siu Tung Wong, Carlo CampajolaRL ControlProximal Policy Optimization

    Institute of Finance and Technology, University College London · Institute of Finance and Technology University College London London, United Kingdom · UZH Blockchain Center +1

  16. Memory-State Critic for Asymmetric Actor-Critic with Application to Vision-Based Pursuit-Evasion

    Oct 2, 2026Arthur Louette, Alejandro Sánchez Roncero, Gaspard Lambrechts +4Asynchronous Advantage Actor-CriticActor-Critic Methods

    University of Liège, Belgium · KTH Royal Institute of Technology, Sweden · McGill University & Mila Québec AI Institute, Canada +1

  17. ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning

    Sep 30, 2026Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov +1RL BenchmarksReinforcement Learning

    Innopolis University, Innopolis, Russia · MIRIAI, Moscow, Russia · Cognitive AI Systems Lab, Moscow, Russia

  18. Whole-Body Aerial Grasping and Lifting via Partial Visual Observations

    Sep 30, 2026Jiaye Jin, Rui Jin, Xinhang Xu +6Aerial RoboticsPartially Observable RL

    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · College of Electronics and Information Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China · NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, and VinUniversity, Hanoi, Vietnam

  19. Belief-Based Maximum Occupancy Principle and Active Inference

    Sep 30, 2026Manolis Mylonas, Rubén Moreno BoteBelief-Space PlanningIntrinsic Reward Methods

    Center for Brain and Cognition & Department of Engineering Universitat Pompeu Fabra Barcelona, Spain

  20. Reinforcement Learning with Complex (valued) Memories

    Sep 29, 2026Sathya Kamesh Bhethanabhotla, Efstratios Gavves, André BiedenkappPartially Observable RLRecurrent Neural Networks

    University of Freiburg

  21. Escaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning

    Sep 28, 2026Yijie Sun, Sanquan Sun, Yanda Zhu +3Reinforcement LearningPartially Observable RL

    Nanjing University · Hong Kong Polytechnic University · Shenzhen University

  22. OSCC: Certified Observation-Safe Coupling Optimization for Gradient-Noise Control in Imperfect-Information Learning

    Sep 27, 2026Miaobo Hu, Shuhao Hu, Xiaobo Guo +4Partially Observable RLPolicy Gradient Methods

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

  23. Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets

    Sep 27, 2026Shuze Daniel Liu, Claire Chen, Jiuqi Wang +2Multi-Agent NegotiationAutomated Negotiation

    Massachusetts Institute of Technology, Cambridge, Massachusetts, USA · Purdue University, West Lafayette, Indiana, USA · California Institute of Technology, Pasadena, California, USA +2

  24. HySTAR: Anchored Hypergraphs for Stable Credit Assignment in Cooperative Multi-Agent Reinforcement Learning

    Sep 25, 2026Xinglong Luo, Yuding Zhang, Yuheng Kuang +5Cooperative MARLMulti-Agent Reinforcement Learning

    University of Electronic Science and Technology of China · Independent Researcher

  25. Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving

    Sep 23, 2026Ben Opperman, Eduardo Alonso, Esther MondragónReinforcement LearningGeneralization in RL

    City St George’s, University of London

  26. Minimal Recurrent Behavioral Memory for Imitation under Partial Observability

    Sep 22, 2026Xianyao Li, Fang Xu, Rui Min +2Partially Observable RLImitation Learning

  27. Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective

    Sep 21, 2026Larry Preuett, Qiuyi Zhang, Muhammad Aurangzeb AhmadDistributional RLMarkov Decision Processes

    University of Washington, Tacoma, USA · Google DeepMind, California, USA · University of Washington, Bothell, USA

  28. CoRe-MARL: Cooperative Redistribution Under Unknown Dynamics Using Recurrent Multi-Agent Reinforcement Learning

    Sep 16, 2026Naimur Rahman Chowdhury, Shatabdi Sen Prapti, Md. Salehin Seyam +1Partially Observable RLMulti-Agent Resource Allocation

    Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States · Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

  29. A note on goal-based hierarchical RL

    Sep 13, 2026Kevin MurphyReinforcement LearningHierarchical RL

  30. Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation

    Sep 11, 2026Kowei Shih, Lu Cheng, Zeyu Wang +2Opponent ModelingPartially Observable RL

    Tsinghua University, Beijing, China · Stevens Institute of Technology, New Jersey, USA · University of California, Los Angeles, Los Angeles, USA +2