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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Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
Jul 17, 2026Josef Hoppe, Sarra Bouchkati, Farah Nasr +7Partially Observable RL Robust RL
Computational Network Science, RWTH Aachen University · IAEW, RWTH Aachen University · E.ON impulse GmbH +2
Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning
Jul 15, 2026Andrea Maria Braghin, Nicolò Botteghi, Matteo Tomasetto +2Partially Observable RL Robot Navigation
Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy · MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
When Does Reward Teach State? A Hidden-Automaton Instrument and a Group-Language Warning Signal
Jul 11, 2026James E. AllchinReinforcement Learning Partially Observable RL
Independent Researcher
EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents
Jul 7, 2026Mianqiu Huang, Taofeng Xue, Chong Peng +12RL for GUI Agents Partially Observable RL
1Meituan · 2Fudan University · 3Shanghai Jiao Tong University +1
Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
Jul 6, 2026Enrique Adrian Villarrubia-Martin, David Muñoz-Valero, Luis Rodriguez-Benitez +2Multi-Agent System Optimization Intelligent Transportation Systems
Department of Technologies and Information Systems, Universidad de Castilla-La Mancha, Paseo de la Universidad 4, Ciudad Real, 13071, Spain · Department of Technologies and Information Systems, Universidad de Castilla-La Mancha, Avenida Carlos III, s/n, Toledo, 45071, Spain · Warwick Manufacturing Group, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK
ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability
Jul 2, 2026Juarez Monteiro, Nathan Gavenski, Guilherme Lima +3Partially Observable RL LLM Prompting
Kunumi Institute, Belo Horizonte, MG, Brazil · King’s College London, London, United Kingdom
HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
Jun 29, 2026Arshia Rafieioskouei, Tzu-Han Hsu, Matthew Lucas +1Reinforcement Learning Partially Observable RL
Department of Computer Science and Engineering, Michigan State University, MI, USA.
Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning
Jun 18, 2026M. Santos-Pascual, D. Ríos InsuaAdversarial Attacks Partially Observable RL
1Inst. Math. Sciences, Spanish Nat. Research Council, Madrid, Spain · Universidad Autónoma de Madrid, Escuela de Doctorado, Madrid, Spain
Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning
Jun 18, 2026Hsiao-Ru Pan, Bernhard SchölkopfPartially Observable RL Sample-Efficient RL
Max Planck Institute for Intelligent Systems, Tübingen · ELLIS Institute Tübingen · ETH Zürich
Learning-Based Decision Making for Combustion Phasing Control in Multi-Fuel CI Engines with Latent Fuel Reactivity Estimation
Jun 16, 2026Rajasree Sarkar, Aditya Satish Patil, Arunava Banerjee +4Reinforcement Learning Partially Observable RL
Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA. · DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA, 21005.
Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
Jun 16, 2026Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian +1Opponent Modeling Autonomous Cyber Defense
Departmaent of Computer Science Vanderbilt University Nashville, TN
A Hybrid Model-Based and Model-Free Framework for Active Multi-View Viewpoint Optimization in Sonar Target Recognition
Jun 13, 2026Yongkyoon Park, Jane ShinNext-Best-View Planning Partially Observable RL
University of Florida Gainesville, FL, United States of America
Minimax-Optimal Policy Regret in Partially Observable Markov Games
Jun 1, 2026Raman AroraRegret Minimization in RL Imperfect-Information Games
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
May 29, 2026Yike Zhao, Onno Eberhard, Malek Khammassi +2Partially Observable RL Markov Models
EPFL, Lausanne, Switzerland · Max Planck Institute for Intelligent Systems, Tübingen, Germany
The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
May 29, 2026Tobias Lademann, Théo Vincent, Jan Peters +1RL Control Partially Observable RL
Institute for Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt · DFKI GmbH, SAIROL · Department of Computer Science, Technical University of Darmstadt +1
Commit to the Bit: Reactive Reinforcement Learning Done Right
May 27, 2026Onno Eberhard, Claire Vernade, Michael MuehlebachReinforcement Learning Partially Observable RL
Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen · University of Technology Nuremberg.
Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
May 24, 2026Kordel K. France, Ovidiu DaescuNon-Stationary RL Partially Observable RL
Department of Computer Science, University of Texas at Dallas, USA
When Does Adaptive Guidance Help? Belief-Aware Privileged Distillation for Autonomous Driving Under Partial Observability
May 24, 2026Mehmet HaklidirPartially Observable RL Autonomous Driving
TUBITAK BILGEM Artificial Intelligence Institute, Turkey
Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
May 23, 2026Noah Farr, Aryaman Reddi, Carlo D'Eramo +1Reinforcement Learning Partially Observable RL
Technical University of Darmstadt · Zuse School ELIZA · Center for Artificial Intelligence and Data Science, University of Würzburg +1
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
May 21, 2026Taewoon Kim, Vincent François-Lavet, Michael CochezPartially Observable RL Q-Learning
1HumemAI · 2Vrije Universiteit Amsterdam · 3ELLIS Institute Finland & Abo Akademi University
Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents
May 19, 2026Wenjie Tang, Minne Li, Sijie Huang +2Reinforcement Learning Partially Observable RL
College of Computer, National University of Defense Technology · 2Intelligent Game and Decision Lab (IGDL) · Institute of Artificial Intelligence, Xiamen University
JAXenstein: Accelerated Benchmarking for First-Person Environments
May 19, 2026Ruo Yu Tao, George KonidarisRL Benchmarks Partially Observable RL
Department of Computer Science, Brown University, USA
Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning
May 19, 2026Nasehatul Mustakim, Lucas LehnertPartially Observable RL OOD Generalization
Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan, Canada
Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
May 18, 2026Jiayu Li, Enpei Zhang, Dawei Zhou +2Sequential MARL Sequential Decision Making
Stern School of Business · New York University · Department of Computer Science +2
When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State
May 18, 2026Peiying Zhu, Sidi ChangPartially Observable RL Dynamic Pricing
Blossom AI San Francisco, USA · Blossom AI Labs Tokyo, Japan
Privacy Preserving Reinforcement Learning with One-Sided Feedback
May 18, 2026Lin William Cong, Guangyan Gan, Hanzhang Qin +1Reinforcement Learning Partially Observable RL
1Nanyang Technological University · Cornell SC Johnson College of Business · 2National University of Singapore
Dynamic Plasma Shape Control with Arbitrary Sensor Subsets
May 15, 2026D. Sorokin, M. Stokolesov, A. Granovskiy +7RL Control Partially Observable RL
Next Step Fusion, Bertrange, L-8070, Luxembourg · Center for Energy Research, University of California San Diego, CA 92093, USA
DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
May 15, 2026Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen +2Visual Navigation Aerial Robotics
Washington University in St. Louis, United States · RISE Research Institutes of Sweden Climate AI Nordics, Sweden
CaMeRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments
May 14, 2026Hong Hong, Feiyu Liao, Yongheng Liang +3RL for Robotics Reinforcement Learning
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China, and the Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
May 14, 2026Luca Marzari, Enrico MarchesiniNeural Network Verification Partially Observable RL
1TU Wien, Vienna, Austria · 2Massachusetts Institute of Technology, Cambridge (MA), USA

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Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

Josef Hoppe, Sarra Bouchkati, Farah Nasr, Jonathan Krapp+6

Computational Network Science, RWTH Aachen University · IAEW, RWTH Aachen University · E.ON impulse GmbH+2

Partially Observable RL Robust RL Power Systems

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Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior end-to-end reinforcement-learning approaches for partially observable curtailment, this work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller, and evaluates its robustness to measurement noise and grid-parameter mismatch. The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability. With accurate grid parameters, the controller reduces total violation magnitude by 98.9%, and this performance remains nearly unchanged under the tested measurement-noise settings. Grid-model mismatch proves to be more challenging, but the controller still mitigates most violations under the tested mismatch assumptions.

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