Sample-Efficient RL

RL: Reinforcement Learning

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Overview

Sample-efficient reinforcement learning aims to reduce the experience needed to learn useful policies, especially when interaction is slow, costly, or risky. Common approaches improve exploration with optimism or uncertainty estimates, reuse data through replay and demonstrations, learn models for planning, or exploit structure such as hierarchy, symmetry, and known dynamics. These methods are studied in settings from tabular and robust decision processes to MuJoCo and Atari control, robotics, and language-model training with verifiable rewards. The field balances empirical gains and PAC or regret guarantees against challenges such as stale replay, model error, limited data coverage, and differences between sample counts and wall-clock cost.

Latest papers

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253 papers30 papers in the last four weeks, up 150% on the four weeks before. 0.2% of all new papers.

  1. Agentic Episodic Control

    Jun 2, 2025Xidong Yang, Wenhao Li, Junjie Sheng +4Agentic RLReinforcement Learning

    East China Normal University · Tongji University · Shanghai Jiao Tong University +2

  2. Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis

    May 18, 2025Zachary Roch, George Atia, Yue WangReinforcement LearningAverage-Reward RL

    Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, USA. · Department of Computer Science, University of Central Florida, Orlando, Florida, USA.

  3. Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

    May 17, 2025Jiashuo Jiang, Yinyu Ye, Yiming ZongMarkov Decision ProcessesReinforcement Learning

    Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology · Department of Management Science & Engineering, Stanford University

  4. Sample Complexity of Linear Quadratic Regulator Without Initial Stability

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

  5. Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

    Oct 27, 2024Kaiyan Zhao, Yiming Wang, Yuyang Chen +3Experience ReplayDeterminantal Point Process

    School of Computer Science, Wuhan University, Wuhan, China · State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao, China · School of Artificial Intelligence Shenzhen Polytechnic University, China

  6. Adaptive teachers for amortized samplers

    Oct 2, 2024Minsu Kim, Sanghyeok Choi, Taeyoung Yun +7Amortized InferenceRL Exploration

    Mila, KAIST · KAIST · Recursion +1

  7. Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning

    Sep 22, 2024Jia Wan, Sean R. Sinclair, Devavrat Shah +1Regret Minimization in RLMarkov Decision Processes

    Laboratory for Information and Decision Systems, Massachusetts Institute of Technology · Department of Industrial Engineering and Management Sciences, Northwestern University

  8. ProSpec RL: Plan Ahead, then Execute

    Jul 31, 2024Liangliang Liu, Yi Guan, BoRan Wang +5Reinforcement LearningRisk-Sensitive RL

    1The Faculty of Computing, Harbin Institute of Technology, Harbin, China · 2The Artificial Intelligence Institute, Harbin Institute of Technology, ShenZhen, China · 3The Artificial Intelligence Institute, Harbin Institute of Technology, Harbin, China

  9. Reflective Policy Optimization

    Jun 6, 2024Yaozhong Gan, Renye Yan, Zhe Wu +1Policy OptimizationPolicy Gradient Methods

    QiYuan Lab.

  10. Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees

    May 26, 2024Yilei Chen, Vittorio Giammarino, James Queeney +1Adversarial Imitation LearningImitation Learning

    Division of Systems Engineering Boston University Boston, MA 02215, USA · Department of Computer Science Purdue University West Lafayette, IN 47907, USA · Amazon Robotics North Reading, MA 01864, USA +1

  11. Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms

    Apr 4, 2024Miao Lu, Han Zhong, Tong Zhang +1Distributionally Robust RLRobust RL

    †Department of Management Science and Engineering, Stanford University. · ‡Center for Data Science, Peking University. · §Department of Computer Science, University of Illinois Urbana-Champaign.

  12. ETHER: Aligning Emergent Communication for Hindsight Experience Replay

    Jul 28, 2023Kevin Yandoka Denamganaï, Daniel Hernandez, Ozan Vardal +2Reinforcement LearningExperience Replay

    Department of Computer Science University of York York, UK

  13. Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

    Jun 23, 2023Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5Reinforcement LearningCausal RL

    Arizona State University, Arizona, USA · Max Planck Institute for Software Systems, Kaiserslautern, Germany · University of Texas at Austin, Texas, USA +3