Partially Observable RL

RL: Reinforcement Learning

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11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

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Latest papers 81

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Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability
May 14, 2026Yushen Liu, Yin-Jen Chen, Ziyi Chen +4Safety-Critical Control Partially Observable RL
University of Virginia · Google · University of Maryland, College Park +3
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
May 12, 2026Asad Bakija, Florent De Geeter, Julien Brandoit +2Dynamical Systems Reinforcement Learning
Dept. of Electrical Engineering and Computer Science · University of Liège · Liège, Belgium
Engagement Process: Rethinking the Temporal Interface of Action and Observation
May 12, 2026Jialian Li, Yuchen Cao, Junhong Liu +5Partially Observable RL Partially Observable Markov Decision Processes
XPENG ROBOTICS
Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty
May 12, 2026Joykirat Singh, Zaid Khan, Archiki Prasad +5Partially Observable RL Bayesian Inference
UNC Chapel Hill · Microsoft Research · The University of Texas at Austin
Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
May 9, 2026Minmin Zhang, Sina Aghaei, Soroush SaghafianSupervised Fine-Tuning Sequential Decision Making
Harvard University
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
May 8, 2026Nikunj Gupta, James Zachary Hare, Jesse Milzman +2Multi-Agent Coordination Partially Observable RL
University of Southern California, Los Angeles, CA, USA · DEVCOM Army Research Laboratory, Adelphi, MD, USA · DEVCOM Army Research Office, Research Triangle Park, North Carolina, USA
Market-Alignment Risk in Pricing Agents: Trace Diagnostics and Trace-Prior RL under Hidden Competitor State
May 7, 2026Peiying Zhu, Sidi ChangReward Hacking Reinforcement Learning
Blossom AI · Blossom AI Labs
Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
May 7, 2026Cristiano da Costa Cunha, Ajmal Mian, Tim French +1RL Benchmarks Structural Causal Models
Department of Computer Science and Software Engineering, University of Western Australia, 6009 WA Australia
Hidden States as Value Gradients: The Pontryagin Structure of Recurrent Policies
May 6, 2026David Leeftink, Max Hinne, Marcel van GervenRL Control Reinforcement Learning
Department of Machine Learning and Neural Computating Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, the Netherlands.
Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability
May 4, 2026Firas Mohamed Elamine Kiram, Imane Youkana, Rachida Saouli +2Reinforcement Learning Healthcare
Dept. of Information Engineering University of Padova Padova, Italy · LINFI Lab, Dept. of Computer Science University of Biskra Biskra, Algeria
Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks
May 4, 2026Po-Heng Chou, Chiapin Wang, Shou-Yu Chen +1Deep Q-Learning Partially Observable RL
Research Center for Information Technology Innovation (CITI), Academia Sinica (AS), Taipei 11529, Taiwan · Department of Electrical Engineering, National Taiwan Normal University (NTNU), Taipei 106308 Taiwan
Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation
May 4, 2026Sofianos Panagiotis Fotias, Vassilis GaganisRL Control Latent 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
Forager: a lightweight testbed for continual learning with partial observability in RL
May 1, 2026Steven Tang, Xinze Xiong, Anna Hakhverdyan +7RL Benchmarks Loss of Plasticity
Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · CIFAR AI Chair
Active World-Model with 4D-informed Retrieval for Exploration and Awareness
Apr 17, 2026Elaheh Vaezpour, Amirhosein Javadi, Tara JavidiActive Perception World Model Learning
KavAI · UCSD
NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing
Mar 20, 2026Raphael Simon, José Carrasquel, Elli Makdis Antoun +2RL Benchmarks Partially Observable RL
CISS Department, Royal Military Academy, Belgium · AI Lab, Vrije Universiteit Brussel, Belgium
Model-Free Output Feedback Stabilization via Policy Gradient Methods
Jan 27, 2026Ankang Zhang, Ming Chi, Xiaoling Wang +1Dynamical Systems Partially 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
Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation
Sep 24, 2025Raphael Simon, Pieter Libin, Wim MeesReinforcement Learning Partially Observable RL
Cyber Defence Lab, CISS Department Royal Military Academy · AI Lab, Department of Computer Science Vrije Universiteit Brussel
Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments
Jul 24, 2025Gilberto Cunha, Alexandra Ramôa, André Sequeira +2Partially Observable RL Bayesian RL
High-Assurance Software Laboratory (HASLab), INESC TEC, Portugal · Department of Computer Science, University of Minho, Portugal · International Iberian Nanotechnology Laboratory (INL), Portugal
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
Feb 6, 2024Oubo Ma, Yuwen Pu, Linkang Du +5Adversarial Attacks Partially Observable RL
Zhejiang University · Xi’an Jiaotong University · Laboratory for Big Data and Decision +2
Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip
Jul 29, 2023Saki Omi, Hyo-Sang Shin, Namhoon Cho +2RL for Robotics Reinforcement Learning
Space Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg · School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, U.K. · Department of Aerospace Engineering, Seoul National University, Seoul, South Korea
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Date pendingPhilip Jordan, Maryam KamgarpourNash Equilibrium Reinforcement Learning
SYCAMORE, EPFL

May 14, 2026, cs.LGJ/K move · Enter open · S save

Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability

Yushen Liu, Yin-Jen Chen, Ziyi Chen, Tao Wang+3

University of Virginia · Google · University of Maryland, College Park+3

Safety-Critical Control Partially Observable RL Risk-Sensitive RL Feedback Control Safe RL

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Many safety-critical control problems are modeled as risk-sensitive partially observable Markov decision processes, where the controller must make decisions from incomplete observations while balancing task performance against safety risk. Although belief-space planning provides a principled solution, maintaining and planning over beliefs can be computationally costly and sensitive to model specification in practical domains. We propose a lightweight risk-gated reinforcement learning approximation for risk-sensitive control under partial observability. The method constructs a compact finite-history proxy state and learns an action-conditioned predictor of near-term safety violation. This predicted candidate-action risk is used in two complementary ways: as a risk penalty during value learning, and as a decision-time gate that interpolates between optimistic and conservative ensemble value estimates. As a result, low-risk actions are evaluated closer to reward-seeking estimates, while high-risk actions are evaluated more conservatively. We evaluate the approach in two safety-critical partially observable domains: automated glucose regulation and safety-constrained navigation. Across adult and adolescent glucose-control cohorts, the method improves overall glycemic tradeoffs and substantially reduces runtime relative to a belief-space planning baseline. On Safety-Gym navigation benchmarks, it achieves a more favorable reward-cost balance than unconstrained RL and several standard safe-RL baselines. These results suggest that action-conditioned near-term risk can provide an effective local signal for approximate risk-sensitive POMDP control when full belief-space planning is impractical.

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