RL Control

RL: Reinforcement Learning

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Overview

Reinforcement learning control studies how agents can learn actions that steer dynamic systems toward goals while managing costs, constraints, and uncertainty. Common approaches include actor-critic methods such as PPO, SAC, and TD3; model-based learning with predictive control; and offline learning from logged data, often combined with safety filters or physical models. Work spans simulated benchmarks and applications such as robotics, traffic, buildings, power grids, and fluid flow, with evaluation increasingly testing hardware transfer and changing conditions. A central tension is that high simulated reward or average performance does not ensure safe, reliable behavior under real-world disturbances, limited data, or distribution shift.

Latest papers

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276 papers34 papers in the last four weeks, up 13% on the four weeks before. 0.2% of all new papers.

  1. Reinforcement learning for control of partial differential equations

    Oct 29, 2026Nicolò Botteghi, Matteo Tomasetto, Andrea ManzoniRL Control

    MOX - Department of Mathematics, Politecnico di Milano, Milano, Italy · Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy

  2. Value Function-Guided MPPI for Hierarchical Planning and Control in Off-Road Autonomous Navigation

    Oct 28, 2026Anoushka Alavilli, Raymond Song, Guanya Shi +1Off-Road Robot NavigationHierarchical Robot Control

    The authors are with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

  3. Autonomous fixed-wing interception using reinforcement learning

    Oct 27, 2026Daan van Hamersveld, Ewoud Smeur, Iurii KriachkoAerial RoboticsRL Control

  4. Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning

    Oct 27, 2026Martín de Frutos, Baptiste Corban, Claire Bizon Monroc +4Optimal ControlRL Control

    ETSIAE-UPM - School of Aeronautics, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros 3, Madrid, E-28040, Spain · Inria, 48 rue Barrault, Paris, 75013, France · DI ENS, Ecole Normale Supérieure, PSL Research University, Paris, France +3

  5. From Human Intent to In-Orbit Action: A Self-Learning Large Language Model Agent for 6G Satellite Networks

    Oct 25, 2026Zhengqian Xu, Yasheng Zhang, Chenhua Sun +2LEO Satellite Networks6G Networks

    The 54th Research Institute of China Electronics Technology Group Corporation (CETC-54)

  6. Reinforcement Learning for Adaptive Crowd Flow Control Using Dynamic Barriers

    Oct 25, 2026Alhanof Alolyan, He Wang, Amani AlonayzanRL ControlReinforcement Learning

    Princess Nourah bint Abdulrahman University, Saudi Arabia · University College London, United Kingdom · Independent Researcher

  7. Curriculum-Driven Deep Reinforcement Learning for Autonomous Far-Range Rocket Landing

    Oct 24, 2026Atin Chowdhury, Piyush JoshiRL ControlCurriculum RL

    Department of Computer Science and Engineering, Indian Institute of Information Technology, Sri City, Andhra Pradesh, India

  8. Computer Vision Navigation for an Autonomous Vehicle Model Using Reinforcement Learning

    Oct 24, 2026Dinis Fernandes, Tiago Ribeiro, António Ribeiro +3RL for Autonomous DrivingRL Control

    Lab. Automação e Robótica University of Minho Guimarães, Portugal

  9. Reinforcement Learning for Controller Parameters in the Auto-Bidding System at Pinterest

    Oct 23, 2026Yichu Zhou, Qinyi Chen, Fei Feng +5Automated BiddingRL Control

    Pinterest Inc. Seattle, USA · Pinterest Inc. San Francisco, USA · Pinterest Inc. New York, USA +1

  10. Dual-Adaptive Z-Score PPO: Dynamic Clipping Using Action Confidence and Normalized Advantage Magnitude

    Oct 23, 2026Taha M. Mahmoud, Naima KaabouchProximal Policy OptimizationPolicy Optimization

    Artificial Intelligence Research (AIR) Center, University of North Dakota, Grand Forks, ND, USA

  11. A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment

    Oct 21, 2026Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8RL Control

    Chair of Safety, Performance, and Reliability for Learning Systems, Technical University of Munich · Institute of Automatic Control, Leibniz University Hannover · Institute for Data Science in Mechanical Engineering, RWTH Aachen University +2

  12. Dissimilarity-Based Markov Decision Processes for Stabilizing State Distributions

    Oct 21, 2026Xun Shen, Yuepeng Wang, Daniyal Sajedi-Hosseini +6RL ControlMarkov Decision Processes

    Tokyo University of Agriculture and Technology, Japan · LY Corporation, Japan · Institute of Science Tokyo, Japan +2

  13. ArcFlow: Training Flow Policies for Multiple Solver Budgets

    Oct 20, 2026Hieu Tran, Hong YuFlow-Based Policy LearningRL for Flow Matching Models

    University of Massachusetts Amherst · University of Massachusetts Lowell

  14. Reinforced Iterative Learning Control for elastic joint arms: Guaranteed cross-trajectory generalization under model uncertainties

    Oct 20, 2026Yuri De Santis, Michele Pierallini, Franco Angelini +1Robot Trajectory TrackingIterative Learning Control

    Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Largo Lucio Lazzarino 1, Pisa, 56122, Italy · Dipartimento di Ingegneria dell’Informazione, Università di Pisa, Via G. Caruso 16, Pisa, 56122, Italy

  15. Glass-Box Deep Reinforcement Learning for High-Speed Flow Control

    Oct 19, 2026Ameya D. JagtapRL ControlFeedback Control

    Aerospace Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA

  16. Reinforcement Learning Controllers for Fusion Scenario Development

    Oct 19, 2026Evgeny Adishchev, Maxim Nurgaliev, Dmitry Sorokin +5Feedback ControlRL Control

    Next Step Fusion s.a.r.l.

  17. Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning

    Oct 13, 2026Svar Rajankumar Patel, Kshitij JerathBehavioral Diversity in RLCooperative MARL

    Department of Computer Science, University of Massachusetts Lowell, Lowell, Massachusetts, USA · Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, Lowell, Massachusetts, USA

  18. From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design

    Oct 12, 2026Mariko Storey-Matsutani, Anatoly Zlotnik, Lee Holguin +2Spacecraft Rendezvous and Proximity OperationsRL Control

    Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Los Alamos National Laboratory, Los Alamos, NM, USA

  19. NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning

    Oct 12, 2026Lingfeng Peng, Xiaohui Xie, Yang Bao +1RL ControlRL for LLM Agents

    Tsinghua University

  20. Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient

    Oct 10, 2026Seiji IshiharaRL ControlReinforcement Learning

    School of Science and Engineering, Tokyo Denki University, Saitama, Japan.

  21. Sim-to-Real RL for ASVs using SysID

    Oct 8, 2026Cody Sheltraw, Tsimafei Lazouski, Maani Ghaffari +1Sim-to-Real TransferRobotics Simulation

    University of Michigan, Ann Arbor, MI 48109, USA.

  22. EliteTable: Self-Improving Program Search for Executable Control Policies

    Oct 8, 2026Stuart FarmerProgram SynthesisLLM-Based Program Synthesis

    Casa Dilauro Independent Research (casadilauro.com)

  23. Sample-Efficiency of Kolmogorov-Arnold Networks

    Oct 7, 2026Kevin Riehl, Shaimaa K. El-Baklish, Fan Wu +1Kolmogorov-Arnold NetworksSample-Efficient RL

    ETH Zürich, IVT & Agentic Systems Lab (ASL) · ETH Zürich, IVT · ETH Zürich, Agentic Systems Lab (ASL)

  24. Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient

    Oct 7, 2026Lijun Bo, Yijie Huang, Chenhao LuRL ControlReinforcement Learning

    School of Mathematics and Statistics, Xidian University, Xi’an, 710126, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China · School of Mathematical Sciences, University of Science and Technology of China, Hefei, 230026, China

  25. HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids

    Oct 6, 2026An Dang, Arturo Flores Alvarez, Yu-Ming Chen +5Humanoid Loco-ManipulationWhole-Body Control

    Amazon · University of Michigan · University of California, Los Angeles +1

  26. Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning

    Oct 6, 2026SungJae Ahn, Jeong Woon Lee, Kyoleen Kwak +1RL ControlReinforcement Learning

    College of Software, Kyung Hee University.

  27. 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.

  28. A Unified Dynamics Framework for Reinforcement Learning and Classical Control of a Six-DOF Pipeline-Tracking ROV in NVIDIA Isaac Sim

    Oct 4, 2026Cheng Siong Chin, M. Venkateshkumar, Jianhua ZhangRL ControlUnderwater Robotics

    Faculty of Science, Agriculture, and Engineering, Newcastle University Singapore, Singapore 828608, Singapore · Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India · School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China

  29. 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

  30. Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift

    Oct 2, 2026Julian Lemmel, Pedro D. Wendel Garcia, Taisuke Kobayashi +1RL ControlRecurrent Neural Networks

    Vienna University of Technology · Medical University of Vienna · National Institute of Informatics, Tokyo