Physics-Informed RL

RL: Reinforcement Learning

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Overview

Physics-informed reinforcement learning studies how to use knowledge of system dynamics and physical constraints to help agents learn better control policies. Common approaches include embedding physics in neural dynamics models and world models, adding physics-based rewards or policy regularizers, and using physical priors to shape actions or enforce safety. These methods are applied to continuous control, robotics, power grids, traffic, and energy systems, with evaluation often spanning simulation benchmarks and real-world transfer. A central challenge is making physical guidance useful when models are imperfect or dynamics involve contacts, uncertainty, and changing conditions.

Latest papers

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32 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. Multi-Horizon Future Discovery for Physics-Informed Exploration

    Oct 26, 2026Shuwei Wang, Qiang Guan, Brian Qiu +5RL ExplorationIntrinsic Motivation

    University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences (CASIA) · School of Artificial Intelligence, University of Chinese Academy of Sciences +2

  2. Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization

    Sep 29, 2026Guoqing Zhang, Rafik Hadfi, Takayuki ItoMulti-Agent CoordinationHealthcare

    Graduate School of Informatics, Kyoto University, Kyoto, Japan

  3. Verti-WM: A Physics-Aided Exteroceptive World Model for Off-Road Reinforcement Learning

    Sep 19, 2026Chenhui Pan, Tong Xu, Xuesu XiaoWorld Models for RoboticsWorld Models

    Department of Computer Science, George Mason University

  4. Dynamics-Informed Reinforcement Learning for Agile and Energy-Efficient Locomotion of a Monopedal Hopping Quadcopter

    Sep 14, 2026Ruigang Chen, Qi Zhang, Zhicheng Zhong +3Energy-Efficient Robot LocomotionRobot Locomotion

    Department of Mechanical Engineering and Robotics, Guangdong Technion - Israel Institute of Technology, Shantou, 515063, Guangdong, China. · Department of Mechanical Engineering, Technion-Israel Institute of Technology, Haifa, 3200003, Israel.

  5. Generalizing HVAC Control With Domain Randomized Reinforcement Learning

    Sep 5, 2026Pablo Boitel, Kun ZhangHeating, Ventilation, and Air Conditioning ControlRL Control

    École de technologie supérieure (ÉTS) Montréal, Québec, Canada

  6. LLMs are Few-Shot Decision-Makers: Generalized Context-Aware Microgrid Frequency Control through Prompt Decision Transformer

    Aug 22, 2026Xu Yang, Chenhui Lin, Haotian Liu +3Power SystemsTransformer-Based RL

    State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

  7. Offline Multi-Agent Reinforcement Learning with a Physics-Informed World Model for Cooperative Mixed Traffic Control

    Aug 18, 2026Lu Liu, Chi Xie, Xi XiongIntelligent Transportation SystemsOffline MARL

    College of Transportation, Tongji University Shanghai, China, 201804

  8. Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning

    Aug 16, 2026Priyanshi Singh, Krishna BhatiaFeedback ControlQuantum RL

    Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai, India · QuantumAI Lab Fractal AI Research Mumbai, India

  9. Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control

    Aug 2, 2026Zuyuan Zhang, Vaneet Aggarwal, Tian LanReinforcement LearningPolicy Optimization

    The George Washington University · Purdue University

  10. A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents

    Jul 29, 2026Zhoupeng Shou, Xiaodong Hong, Congjing Ren +3Language Model-Based ControlFeedback Control

    State Key Laboratory of Chemical Engineering, Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China · Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China · Ningbo Innovation Center, Zhejiang University, Zhejiang, China

  11. Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics

    Jul 28, 2026Ya-Chia Shen, Woei-Leong ChanRL ControlQuadrotor Control

    Department of Computer Science and Information Engineering, National Cheng Kung University · Department of Aeronautics and Astronautics, National Cheng Kung University

  12. Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems

    Jul 17, 2026Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni +1RL ControlReinforcement Learning

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

  13. Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum

    Jul 15, 2026Slava AndrejevRL ControlReinforcement Learning

  14. Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design

    Jul 13, 2026Yifei Chen, Shihan Lu, Ed Colgate +1Robotic Hand DesignRobotic Grasping

    Center for Robotics and Biosystems, Northwestern University

  15. Physics-Guided Biomechanical Gait Adaptation for Humanoid Locomotion on Extreme Sloped Terrains

    Jul 8, 2026Xuanyu Chen, Mohan Liu, Dengchen Mei +6Humanoid Robot LocomotionReinforcement Learning

    Nanyang Technological University · A*STAR

  16. Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids

    Jul 6, 2026Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat +1Reinforcement LearningFederated RL

    iSTAR Lab, Texas A&M University College Station, TX, USA

  17. High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning

    Jul 3, 2026Yanzhou Li, Guangli Chen, Xiao-Meng Li +3Multi-Robot Formation ControlMulti-Robot Systems

    Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University, Dongguan 523000, China · Chair of Applied Statistics, Technische Universität Dresden, Dresden, Germany · School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China +2

  18. Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

    Jul 3, 2026Georg Schäfer, Jakob Rehrl, Stefan HuberRL ControlReinforcement Learning

    Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Salzburg, Austria · Paris Lodron University of Salzburg, Salzburg, Austria

  19. TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

    Jun 16, 2026Zijie Meng, Ziwei Li, Yufei Liu +5Cyber-Physical SystemsReinforcement Learning

    Peking University · National Taiwan University · WHU +1

  20. MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

    Jun 5, 2026Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu +10Materials Property PredictionCrystal Structure Generation

    State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, China. · Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China. +5

  21. Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics

    May 28, 2026Vittorio Giammarino, Anastasios Manganaris, Ahmed H. QureshiReinforcement LearningContact-Rich Robotic Manipulation

    Department of Computer Science Purdue University

  22. QuietWalk: Physics-Informed Reinforcement Learning for Ground Reaction Force-Aware Humanoid Locomotion Under Diverse Footwear

    Apr 26, 2026Hanze Hu, Luying Feng, Silu Chen +6Humanoid Robot LocomotionRobot Locomotion

    Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China. · University of Chinese Academy of Sciences, Beijing 100049, China. · Zhejiang Key Laboratory of Precision Actuation and Intelligent Robotics. +1

  23. Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing

    Apr 10, 2026Shathushan Sivashangaran, Apoorva Khairnar, Sepideh Gohari +2Autonomous RacingRL Control

    Autonomous Robots and Vehicles Laboratory, College of Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA. · Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA. · Robotics Department, University of Michigan, Ann Arbor, MI 48109, USA.

  24. Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids

    Apr 2, 2026Pantelis Dogoulis, Maxime CordyRL ControlReinforcement Learning

    SerVal, SnT, University of Luxembourg, Luxembourg City, Luxembourg

  25. PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning

    Mar 26, 2026Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin AlamdariContextual RLAdaptive Sensing

    Department of Civil and Environmental Engineering Florida State University, Tallahassee, FL, USA

  26. Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing

    Mar 18, 2026Aniruddha Bora, Julie Chalfant, Chryssostomos ChryssostomidisMaritime TransportationOffline RL

    Department of Computer Science, Texas State University, San Marcos, TX 78666, USA · MIT Sea Grant College Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

  27. 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

  28. Data-Driven Physics Embedded Dynamics with Predictive Control and Reinforcement Learning for Quadrupeds

    Mar 15, 2026Prakrut Kotecha, Aditya Shirwatkar, Shishir KolathayaLagrangian Neural NetworksRL for Legged Locomotion

    the Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bengaluru. · the Robert Bosch Center for Cyber-Physical Systems and the Department of Computer Science & Automation, Indian Institute of Science, Bengaluru.

  29. 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

  30. DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration

    Mar 8, 2026Jinzhou Tang, Fan Feng, Minghao Fu +3World Model LearningWorld Models

    UC San Diego · Sun Yat-sen University