Continuous-Time RL

RL: Reinforcement Learning

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Overview

Continuous-time reinforcement learning studies how to choose actions and their timing when states evolve between decisions, as in physical control, finance, and medical treatment. Methods include actor–critic and policy-gradient algorithms, Q-learning, model-based approaches that estimate diffusion generators, and entropy regularization for exploration and robustness. Researchers apply these methods to stochastic differential equations, jump processes, mean-field systems, and optimal stopping, evaluating them on control, pricing, and treatment tasks. The field has growing theoretical guarantees for convergence and regret, but practical learning still depends on approximations to dynamics and value functions, with open challenges in data efficiency, safety, and path-dependent systems.

Latest papers

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34 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

  1. Frequency-Uniform Log-Regret Reinforcement Learning for Diffusions

    Oct 27, 2026Jose Blanchet, Lutong Hao, Renyuan XuRegret Minimization in RLModel-Based RL

    Department of Management Science and Engineering, Stanford University.

  2. Reinforcement learning for constrained stochastic linear--quadratic control in continuous time

    Oct 26, 2026Weiping Wu, Qitong Pan, Xun Li +1Stochastic Optimal ControlLinear Quadratic Regulator

    School of Economics and Management, Fuzhou University, Fuzhou, 350108, Fujian, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China

  3. A Model-Free Continuous-Time RL Algorithm from Discrete-Time Off-Policy Data with End-to-End Guarantees

    Oct 20, 2026Chaorui Yao, Yuming Paul Zhang, Lin Yang +1Continuous-Time RLModel-Free RL

    Department of Electrical and Computer Engineering, University of California, Los Angeles. · Department of Mathematics and Statistics, Auburn University. · Department of Statistics and Data Science, University of California, Los Angeles.

  4. Continuous-Time Policy Gradient from Discrete Data: Convergence Analysis and Model-Free Algorithms

    Oct 20, 2026Martin Hernandez, Xinyan Qing, Mo Zhou +1Continuous-Time RLModel-Free RL

    Department of Statistics and Data Science, University of California, Los Angeles · Department of Statistics, University of Michigan · School of Mathematical Sciences, Peking University

  5. 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

  6. Amortized Score-Hamiltonian Policy Iteration: A Grid-Free Scheme for Relaxed Stochastic Control Problems

    Oct 3, 2026Qi Feng, Gu WangStochastic Optimal ControlPolicy Iteration

    Department of Mathematics, Florida State University, Tallahassee, 32306 · Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA 01609

  7. Provably Efficient Reinforcement Learning in Continuous-Time Episodic MDPs with Poisson Decision Epochs

    Sep 19, 2026Kenny Guo, Valentio Iverson, Sahan Wijetunga +1Markov Decision ProcessesContinuous-Time RL

    Department of Economics, Yale University, New Haven, Connecticut, USA · Department of Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA · Department of Mathematics, University of California, Los Angeles, Los Angeles, California, USA

  8. Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies

    Sep 17, 2026Juncheng YiContinuous-Time RLPolicy Gradient Methods

  9. Learning to Solve Stochastic Controls with Unknown Drifts and Running Rewards: Theory, Algorithms and Convergence

    Sep 14, 2026Jin Ma, Gaozhan Wang, Jianfeng Zhang +1Reinforcement LearningStochastic Optimal Control

    Department of Mathematics, University of Southern California, Los Angeles, CA 90089 · Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027

  10. Reinforcement Learning for Continuous-Time Jump Markov Decision Processes with Applications to Network Dynamic Pricing

    Aug 21, 2026Huiling Meng, Ningyuan Chen, Xuefeng GaoMarkov Decision ProcessesContinuous-Time RL

    Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China. · Rotman School of Management, University of Toronto, Toronto, Canada

  11. Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions

    Aug 19, 2026Tomasz R. Bielecki, Thibaut Mastrolia, Haoze YanStochastic Optimal ControlContinuous-Time RL

    Hinsdale, Illinois, USA · UC Berkeley, Department of Industrial Engineering and Operations Research, USA

  12. Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment

    Jul 31, 2026Yanwei Jia, Du OuyangMulti-Armed BanditsRegret Minimization in RL

    Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR · Department of Mathematical Sciences, Tsinghua University, Beijing, Beijing 100084, China

  13. A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

    Jul 16, 2026Zikun Zhang, Jiayuan Sheng, David D. Yao +1Masked Diffusion ModelsRL for Language Model Reasoning

    Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027

  14. Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies

    Jul 13, 2026Ziheng Cheng, Xin Guo, Huyên Pham +1Policy GradientContinuous-Time RL

    University of California, Berkeley · Ecole Polytechnique, CMAP · Imperial College London

  15. Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning

    Jul 3, 2026Jialun Cao, Fernando Acero, David Šiška +1Robust Markov Decision ProcessesMaximum Entropy RL

    School of Mathematics, University of Edinburgh, United Kingdom. · J.P. Morgan AI Research, United Kingdom. · Department of Mathematics, Imperial College London, United Kingdom.

  16. Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models

    Jun 27, 2026Congde Hu, Danping Li, Lin Xu +1Stackelberg GamesMaximum Entropy RL

    School of Mathematics and Statistics, Anhui Normal University, Wuhu, Anhui 241002, China · School of Statistics, Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, Faculty of Economics and Management, East China Normal University, Shanghai, China · School of Mathematics, Southeast University, Nanjing, Jiangsu 211189, China

  17. Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data

    Jun 25, 2026Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1Actor-Critic MethodsStochastic Optimal Control

    Department of Mathematics, University of Michigan, Ann Arbor, MI, USA. · Department of Statistics and Data Science, University of California, Los Angeles, CA, USA. · Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.

  18. Continuous-time Optimal Stopping through Deep Reinforcement Learning

    Jun 16, 2026Cosmin Borsa, Michael LudkovskiQuantitative FinanceSequential Decision Making

    Department of Statistics & Applied Probability, UC Santa Barbara. Santa Barbara, CA 93106-3110, USA

  19. Deep Q-Learning on Hölder Spaces

    Jun 15, 2026Qian QiRL ControlDeep Q-Learning

    Peking University

  20. Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems

    Jun 10, 2026Xin Guo, Yijie Huang, Xiang YuPolicy GradientStochastic Optimal Control

    Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA. · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.

  21. Policy Gradient for Continuous-Time Robust Markov Decision Processes

    Jun 3, 2026Tanya Veeravalli, David M. Bossens, Atsushi NitandaRobust Markov Decision ProcessesMarkov Decision Processes

    Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR) · Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR) · College of Computing and Data Science, Nanyang Technological University

  22. From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments

    Jun 2, 2026Saket Tiwari, Tejas Kotwal, George KonidarisRL ControlReinforcement Learning

    Department of Computer Science Brown University · Department of Applied Mathematics Brown University

  23. Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment

    May 31, 2026Yuepeng Wang, Ken Kawano, Akifumi Wachi +13Reinforcement LearningSequential Decision Making

    Tokyo University of Agriculture and Technology · Institute of Science Tokyo · LY Corporation +6

  24. A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning

    May 31, 2026Yuepeng Wang, Ken Kawano, Yoshihiko Fujisawa +12RL BenchmarksReinforcement Learning

    Tokyo University of Agriculture and Technology · Institute of Science Tokyo · National University of Singapore +5

  25. Global linear convergence of entropy-regularized softmax policy gradient beyond tabular MDPs

    May 24, 2026Ziyue Chen, David Šiška, Lukasz SzpruchPolicy GradientReinforcement Learning

  26. Image AID via continuous-time reinforcement learning

    May 13, 2026Yilie Huang, Xun Yu ZhouDiffusion Model GuidanceMasked Diffusion Models

    Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. · Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University, New York, NY 10027, USA.

  27. Improved Model-based Reinforcement Learning with Smooth Kernels

    May 8, 2026Kun Long, Yuqiang Li, Xianyi WuReinforcement LearningContinuous-Time RL

    School of Statistics, KLATASDS-MOE, East China Normal University, North Zhongshan Road, Shanghai, 200062, PR China.

  28. Operator-Guided Invariance Learning for Continuous Reinforcement Learning

    May 7, 2026Zuyuan Zhang, Fei Xu Yu, Tian LanReinforcement LearningContinuous-Time RL

    The George Washington University

  29. Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms

    Apr 30, 2026Zhenjie Ren, Xiaoli Wei, Xiang Yu +1Reinforcement LearningStochastic Optimal Control

    LaMME, Universit´e ´Evry Paris-Saclay, ´Evry, France · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong · Department of Industrial Engineering and Operations Research, Columbia University, New York, USA

  30. Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions

    Apr 6, 2026Daniel BlochNon-Markovian RLReinforcement Learning

    UNIVERSITY OF PARIS 6 & VINUNIVERSITY · Visting Professor at the College of Engineering and Computer Science, VinUniversity, Hanoi.