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. Adapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning

    Mar 15, 2026Jingyi Liu, Jian Guo, Eberhard GillLoss Landscape GeometryRL Control

    Delft University of Technology, Delft, 2629 HS, the Netherlands

  2. Visualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms

    Mar 15, 2026Jingyi Liu, Jian Guo, Eberhard GillRL ControlActor-Critic Methods

    Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, Delft, 2629 HS, The Netherlands

  3. Physics-Informed Policy Optimization via Analytic Dynamics Regularization

    Mar 15, 2026Namai Chandra, Liu Mohan, Zhihao Gu +1Physics-Informed RLRL Control

    Electronic Systems, IIT Madras, India · EmPACT Lab, Nanyang Technological University, Singapore

  4. Reinforcement Learning for Elliptical Cylinder Motion Control Tasks

    Mar 13, 2026Pawel Marczewski, Paulina Superczynska, Jakub Bernat +1RL ControlFeedback Control

    Institute of Computing Science Poznan University of Technology Poznan, 61-138 · Institute of Automatic Control and Robotics Poznan University of Technology Poznan, 61-138

  5. Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning

    Mar 12, 2026Taeho Lee, Donghwan LeeAdversarial MARLReinforcement Learning

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

  6. Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application

    Mar 12, 2026Alaaeddine Chaarani, Narcis Palomeras, Pere RidaoUnderwater RoboticsRL Control

    Authors with the Faculty of Computer Science, Computer Vision and Robotics Research Group (vicorob) Universitat de Girona, 17003 Girona, Spain.

  7. Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization

    Mar 12, 2026Qijun Liao, Jue Yang, Yiting Kang +3RL ControlReinforcement Learning

    School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China · Jiangsu XCMG Construction Machinery Research Institute Co., Ltd., Jiangsu 221000, China

  8. Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation

    Mar 11, 2026Ilseung Park, Eunsik Choi, Jangwhan Ahn +1Musculoskeletal ModelingRL Control

    Carnegie Mellon University · Department of Physical Education, Seoul National University, Seoul, Republic of Korea · Seoul National University +2

  9. Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces

    Mar 9, 2026Hamish Flynn, Joe Watson, Ingmar Posner +1RL ControlRegret Minimization in RL

    Carnegie Mellon University · University of Oxford · Technical University of Darmstadt +2

  10. Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system

    Mar 8, 2026Heungjo AnRL ControlPower Systems

    Department of Industrial Engineering, Kumoh National Institute of Technology, Gumi City, South Korea

  11. Learning From Failures: Efficient Reinforcement Learning Control with Episodic Memory

    Mar 7, 2026Chenyang MiaoReinforcement LearningRL Control

    Xi’an Jiaotong University

  12. How to Model Your Crazyflie Brushless

    Mar 6, 2026Alexander Gräfe, Christoph Scherer, Wolfgang Hönig +1Quadrotor ControlRobotics Simulation

    Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Germany · Intelligent Multi-Robot Coordination Lab, TU Berlin, Germany

  13. Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control

    Mar 4, 2026Nicolas Helson, Pegah Alizadeh, Anastasios GiovanidisConservative RLOffline RL

    Ericsson Research, Massy, France

  14. State-Action Inpainting Diffuser for Continuous Control with Delay

    Mar 2, 2026Dongqi Han, Wei Wang, Enze Zhang +1Delayed RLReinforcement Learning

    Microsoft Research Asia · University of Pennsylvania · Shanghai Jiaotong University

  15. RF-Agent: Automated Reward Function Design via Language Agent Tree Search

    Feb 27, 2026Ning Gao, Xiuhui Zhang, Xingyu Jiang +3Reward Design for RLRL Control

    Beihang University, 37 Xueyuan Road, Haidian District, Beijing

  16. Two-Stage Active Distribution Network Voltage Control via LLM-RL Collaboration: A Hybrid Knowledge-Data-Driven Approach

    Feb 25, 2026Xu Yang, Chenhui Lin, Xiang Ma +4Power SystemsRL Control

    Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China. · State Grid Zhejiang Electric Power Co., Ltd., Jinhua Power Supply Company, Jinhua 321017, China.

  17. Learning End-to-End Control for Omnidirectional Aerial Motion on Overactuated Tilt-rotor Quadrotors

    Feb 25, 2026Wentao Zhang, Zhaoqi Ma, Jinjie Li +8Quadrotor ControlRL Control

    DRAGON Lab, Department of Mechanical Engineering, The University of Tokyo, Tokyo, 113-8656, Japan · School of Computing and Data Science, The University of Hong Kong, 999077, HKSAR · Autonomous Systems Lab(ASL), ETH Zurich, 8092 Zurich, Switzerland

  18. Learning to Tune Pure Pursuit in Autonomous Racing: Joint Lookahead and Steering-Gain Control with PPO

    Feb 20, 2026Mohamed Elgouhary, Amr S. El-WakeelAutonomous RacingAdaptive Control

    The authors are with the Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

  19. Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty

    Feb 20, 2026Zhaoming Xie, Kevin Karol, Jessica HodginsRL ControlRobot Imitation Learning

    Robotics AI Institute, Cambridge, Massachusetts, USA

  20. Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow

    Feb 16, 2026Minh NguyenReinforcement LearningContinuous-Time RL

    Google · University of Texas at Austin

  21. Agile Reinforcement Learning through Separable Neural Architecture and Applications

    Jan 30, 2026Rajib Mostakim, Reza T. Batley, Sourav SahaRL ControlNeural Network Approximation Theory

    Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

  22. The HydroGym Reinforcement Learning Platform for Fluid Dynamics

    Dec 19, 2025Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18RL ControlRL Benchmarks

    Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States · AI Institute in Dynamic Systems, University of Washington, Seattle, WA 98195, United States · Chair of Fluid Mechanics and Institute of Aerodynamics, RWTH Aachen University, Aachen, Germany +9

  23. Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes

    Dec 17, 2025Hanqing Jin, Renyuan Xu, Yanzhao YangRL ControlReinforcement Learning

    Mathematical Institute, University of Oxford · Department of Management Science and Engineering, Stanford University

  24. Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes

    Dec 9, 2025Lauritz Rismark Fosso, Herman Biørn Amundsen, Marios Xanthidis +1RL ControlSim-to-Real Transfer

    SINTEF Ocean, Trondheim, Norway

  25. Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

    Dec 1, 2025Sebastian Sanokowski, Kaustubh Patil, Majid KhadivRL ControlDiffusion Policy

    Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University Munich · Practical Project Student Exchange Program, Technical University Munich · MIT World Peace University

  26. A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms

    Nov 4, 2025Linxin Hou, Qirui Wu, Zhihang Qin +3RL ControlRobotic Policy Evaluation

    Department of Electrical and Computer Engineering, National University of Singapore · Department of Computer Science, National University of Singapore · Department of Mechanical Engineering, National University of Singapore

  27. TARC: Time-Adaptive Robotic Control

    Oct 27, 2025Arnav Sukhija, Lenart Treven, Jin Cheng +3Robotic ControlRL Control

    ETH Zurich

  28. Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA Framework

    May 5, 2025Andrzej Mizera, Jakub ZarzyckiRL ControlAsynchronous RL

    Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland · IDEAS Research Institute, Królewska 27, 00-060 Warsaw, Poland · IDEAS NCBR Sp. z o.o., Chmielna 69, 00-801 Warsaw, Poland

  29. TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels

    Apr 17, 2025Siow Meng Low, Ze Gong, Akshat KumarRL ControlCredit Assignment in RL

    School of Computing and Information Systems, Singapore Management University, Singapore · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

  30. Sample Complexity of Linear Quadratic Regulator Without Initial Stability

    Feb 20, 2025Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman GharesifardRL ControlSample Complexity