Reinforcement Learning

Also known as RL

Reinforcement learning studies how an agent can choose actions over time to earn rewards, using experience to improve its decisions. Common approaches include value-based methods such as Q-learning, policy-gradient methods such as PPO, and model-based methods that learn or use a model of the environment; offline and hierarchical methods address learning from fixed data and long tasks. Applications range from Atari and MuJoCo benchmarks to robot control, traffic, games, and language-model agents that use tools or solve multi-step problems. A central challenge is making learning efficient and reliable when rewards are sparse or imperfect, environments change, and success in simulation or benchmarks may not transfer safely to deployment.

Latest papers

1,767 papers195 papers in the last four weeks, down 8% on the four weeks before. 1.3% of all new papers.

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs
May 2, 2026Ruiquan Huang, Donghao Li, Yingbin Liang +1Markov Decision Processes Reinforcement Learning
University of Kentucky. · University of Virginia. · Ohio State University..
Faithful Mobile GUI Agents with Guided Advantage Estimator
May 2, 2026Haowen Hu, Pengzhou Cheng, Zheng Wu +3Reinforcement Learning RL for GUI Agents
1Shanghai Jiao Tong University.
Forager: a lightweight testbed for continual learning with partial observability in RL
May 1, 2026Steven Tang, Xinze Xiong, Anna Hakhverdyan +7RL Benchmarks Loss of Plasticity
Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · CIFAR AI Chair
Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
May 1, 2026Andrzej Ruszczynski, Tiangang ZhangMarkov Decision Processes Reinforcement Learning
Department of Management Science and Information Systems, Rutgers University
Recovering Hidden Reward in Diffusion-Based Policies
May 1, 2026Yanbiao Ji, Qiuchang Li, Yuting Hu +7Reinforcement Learning Diffusion Policy
Shanghai Jiao Tong University · KOKONI 3D
Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
May 1, 2026Xin-Ye Li, Ren-Biao Liu, Yun-Ji Zhang +3RL for Code Generation Reinforcement Learning
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization
May 1, 2026Yue Mao, Shicheng Liu, Siyuan Xu +1Reinforcement Learning
Department of Mechanical Engineering, Pennsylvania State University · Department of Electrical Engineering, Pennsylvania State University
Model-Based Reinforcement Learning with Double Oracle Efficiency in Policy Optimization and Offline Estimation
May 1, 2026Haichen Hu, Jian Qian, David Simchi-LeviRegret Minimization in RL Markov Decision Processes
Laboratory for Information and Decision Systems · Department of AI and Data Science
Interpretable experiential learning based on state history and global feedback
May 1, 2026Anton KoloninReinforcement Learning Game-Playing Agents
The Artificial Intelligence Research Center, Novosibirsk State University, Russia · Aigents, Russia
ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning
May 1, 2026Zihan Lin, Xiaohan Wang, Jie Cao +6RL for Language Models Reinforcement Learning
MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China · Meituan, Beijing, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China.
Data Deletion Can Help in Adaptive RL
Apr 30, 2026Param Budhraja, Aditya Gangrade, Alex Olshevsky +1Non-Stationary RL Reinforcement Learning
Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA · Faculty of Electrical and Computer Engineering, Systems Engineering, Boston University, Boston, MA 02215, USA
Pessimism-Free Offline Learning in General-Sum Games via KL Regularization
Apr 30, 2026Claire Chen, Yuheng ZhangNash Equilibrium Reinforcement Learning
The Division of Physics, Mathematics and Astronomy California Institute of Technology · Department of Computer Science University of Illinois Urbana-Champaign
Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
Apr 30, 2026Yikai Wang, Shang Liu, Jose BlanchetReward Over-Optimization Regret Minimization in RL
Department of Statistics and Operations Research, University of North Carolina · Imperial Business School, Imperial College London · Department of Management Science and Engineering, Stanford University
RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses
Apr 30, 2026Feiyu Wu, Xu Zheng, Zhuocheng Wang +2Reinforcement Learning LLM-Guided RL
School of Cyber Engineering, Xidian University
Latent-GRPO: Group Relative Policy Optimization for Latent Reasoning
Apr 30, 2026Jingcheng Deng, Zihao Wei, Liang Pang +4Reinforcement Learning RL for Language Model Reasoning
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences
GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
Apr 30, 2026Junan Hu, Jian Liu, Jingxiang Lai +6Reinforcement Learning RL for GUI Agents
1Shandong University · 2The Hong Kong University of Science and Technology · 3The Hong Kong University +2
Leveraging Verifier-Based Reinforcement Learning in Image Editing
Apr 30, 2026Hanzhong Guo, Jie Wu, Jie Liu +6Reward Modeling Reinforcement Learning
School of Computing and Data Science, The University of Hong Kong · ByteDance Seed · CUHK +1
PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations
Apr 30, 2026Yang Zhang, Jiangyuan Zhao, Chenyou Fan +11Reinforcement Learning Long-Horizon Robotic Manipulation
Institute of Artificial Intelligence (TeleAI), China Telecom · Tsinghua University · Shanghai Jiao Tong University +1
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 Learning Stochastic 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
Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction
Apr 30, 2026Wentao Chen, Jingtang Chen, Mingjian Fu +4Reinforcement Learning UAV Navigation
AutoREC: A reinforcement learning platform for equivalent circuit model generation
Apr 29, 2026Ali Jaberi, Yonatan Kurniawan, Robert Black +5Reinforcement Learning System Identification
Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada · Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada · Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada +1
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Apr 29, 2026Mahya Ramezani, Holger VoosReinforcement Learning UAV Navigation
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.
Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding
Apr 29, 2026Hayate Iso, Tiyasa Mitra, Sudipta Mondal +15Reinforcement Learning Speculative Decoding
NVIDIA
FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
Apr 29, 2026Zhixin Han, Yanzhi Zhang, Chuyang Wei +11Agentic RL Reinforcement Learning
College of Software, Nankai University · Zhongguancun Academy, Beijing, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences +3
Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Apr 29, 2026Seungyub Han, Hyungjin Kim, Jungwoo LeeReinforcement Learning Offline RL
Seoul National University
Uncertainty-Aware Reward Discounting for Mitigating Reward Hacking
Apr 29, 2026Disha SinghaReward Hacking Reinforcement Learning
Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
Apr 29, 2026Bolian Li, Yifan Wang, Yi Ding +3Entropy Collapse in RL RL for Language Models
Purdue University, West Lafayette, IN, USA
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
Apr 29, 2026Tianhao Hu, Xiangcheng Liu, Youshao Xiao +15RL for Language Models Asynchronous RL
Meituan, China
How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
Apr 28, 2026Chu-Cheng Lin, Eugene IeSupervised Fine-Tuning Reinforcement Learning
Google
Sample-efficient Neuro-symbolic Proximal Policy Optimization
Apr 28, 2026Simone Murari, Celeste Veronese, Daniele MeliReinforcement Learning Proximal Policy Optimization
Dept. of Computer Science, University of Verona, Italy

May 2, 2026J/K move · Enter open · S save

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs

Ruiquan Huang, Donghao Li, Yingbin Liang, Jing Yang

University of Kentucky. · University of Virginia. · Ohio State University..

Markov Decision Processes Reinforcement Learning Linear Markov Decision Processes Actor-Critic Methods Policy Learning Sample-Efficient RL

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Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions with an unknown environment. In settings with function approximation, many existing RL algorithms achieve favorable sample complexity, but often rely on computationally intractable oracles. In this paper, we use supervised learning as a computational proxy to establish a clear hierarchy of commonly adopted RL oracles under low-rank Markov Decision Processes (MDPs). This hierarchy shows that policy evaluation is the most computationally efficient oracle, provided that supervised learning can be efficiently solved. Motivated by this observation, we propose a novel optimistic actor-critic algorithm that relies solely on the policy evaluation oracle. We prove that our algorithm outperforms the existing sample complexity guarantees for low-rank MDPs while avoiding computationally expensive planning or optimization oracles commonly assumed in prior works. We further extend our theoretical results to approximately low-rank MDPs and demonstrate that this setting captures a broad class of real-world environments. Finally, we validate our theoretical results with experiments on several standard Gym environments.