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

1,743 papers237 papers in the last four weeks, up 46% on the four weeks before. 1.4% of all new papers.

Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
Mar 26, 2026Ziyan Wang, Peng Chen, Ding Li +4Autonomous Driving Simulation Reinforcement Learning
State Key Laboratory of Intelligent Transportation System, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents
Mar 25, 2026Guihlerme Daubt, Adrian RedderSafe RL Safe Robot Navigation
Faculty of Technology, Bielefeld University, Germany · Department of Electrical Engineering and Information Technology, Paderborn University, Germany
Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization
Mar 25, 2026Fei Bai, Zhipeng Chen, Chuan Hao +6RL for Language Model Reasoning Reinforcement Learning
Gaoling School of Artificial Intelligence, Renmin University of China · IQuest Research · Beihang University
From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
Mar 25, 2026Lijing Luo, Yiben Luo, Alexey Gorbatovski +2RL Benchmarks Reinforcement Learning
Sun Yat-sen University Shenzhen, China · Yancheng Institute of Technology Yancheng, China · Central University Moscow, Russia +1
Self Paced Gaussian Contextual Reinforcement Learning
Mar 24, 2026Mohsen Sahraei Ardakani, Rui SongContextual RL Reinforcement Learning
Department of Electrical and Computer Engineering North Carolina State University Raleigh, NC · Department of Statistics North Carolina State University Raleigh, NC
Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL
Mar 24, 2026Igor JankowskiSemi-Markov Decision Process Reinforcement Learning
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
Mar 24, 2026Zakaria Mhammedi, Alexander Rakhlin, Nneka OkoloReinforcement Learning Linear Markov Decision Processes
Google Research, NYC · MIT
Off-Policy Value-Based Reinforcement Learning for Large Language Models
Mar 24, 2026Peng-Yuan Wang, Ziniu Li, Tian Xu +8Reinforcement Learning RL for Language Model Reasoning
National Key Laboratory for Novel Software Technology & School of Artificial Intelligence, Nanjing University, China · The Chinese University of Hong Kong, Shenzhen · Shenzhen Research Institute of Big Data +1
EVA: Efficient Reinforcement Learning for End-to-End Video Agent
Mar 24, 2026Yaolun Zhang, Ruohui Wang, Jiahao Wang +6Video Understanding Temporal Video Understanding
SenseTime Research
CoMaTrack: Competitive Multi-Agent Game-Theoretic Tracking with Vision-Language-Action Models
Mar 24, 2026Youzhi Liu, Li Gao, Liu Liu +2Game-Theoretic RL Reinforcement Learning
Amap, Alibaba Group
Human-Inspired Pavlovian and Instrumental Learning for Autonomous Agent Navigation
Mar 23, 2026Jingfeng Shan, Francesco Guidi, Mehrdad Saeidi +8Reinforcement Learning Model-Based RL
WiLab, University of Bologna, via dell’Università 50, 47521 Cesena, Italy · National Research Council of Italy (CNR), viale del Risorgimento 2, 40136 Bologna, Italy · University of Bologna, Department of Psychology “Renzo Canestrari”, 47521 Cesena, Italy
A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP
Mar 23, 2026Xi Yang, Aurelie Lozano, Naoki Abe +6LLM Agents LLM Agent Training
IBM Software Innovation Lab, Yorktown Heights, New York, USA
Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning
Mar 23, 2026Susana Nunes, Tiago Guerreiro, Catia PesquitaExplainable Artificial Intelligence Reinforcement Learning
LASIGE, Faculdade de Ciências da Universidade de Lisboa, Portugal.
Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
Mar 23, 2026Tom Maus, Stephan Frank, Tobias GlasmachersEvolutionary RL Reinforcement Learning
Inst. for Neural Computation, Ruhr University Bochum, Bochum, Germany
PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost
Mar 22, 2026Junkeun Yi, Damon Mosk-Aoyama, Baihe Huang +9RL Post-Training RL for LLM Agents
NVIDIA · UC Berkeley
DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns
Mar 22, 2026Trung V. Phan, Thomas BauschertAutonomous Cyber Defense Reinforcement Learning
Chair of Communication Networks, Technische Universität Chemnitz, 09126 Chemnitz, Germany
VisFly-Lab: Unified Differentiable Framework for First-Order Reinforcement Learning of Quadrotor Control
Mar 22, 2026Fanxing Li, Fangyu Sun, Tianbao Zhang +5Reinforcement Learning Quadrotor Control
Learning to Optimize Joint Source and RIS-assisted Channel Encoding for Multi-User Semantic Communication Systems
Mar 22, 2026Haidong Wang, Songhan Zhao, Bo Gu +3Joint Source-Channel Coding Semantic Communication
School of Intelligent Systems Engineering, Sun Yat-sen University, China · Department of Electrical and Computer Engineering, University of Hong Kong, HongKong, SAR, China · Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Canada
Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
Mar 21, 2026Zelin Wan, Jin-Hee Cho, Mu Zhu +3Cybersecurity Autonomous Cyber Defense
Department of Computer Science, Virginia Tech, Falls Church, VA, USA · Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · US Army Research Laboratory, Adelphi, MD, USA +1
Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling
Mar 21, 2026Faraz Shaikh, Gianluca Reali, Mauro FemminellaRL Control Reinforcement Learning
Department of Engineering, University of Perugia, Perugia, Italy · Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, Italy
Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
Mar 20, 2026Seyed Mahdi B. Azad, Jasper Hoffmann, Iman Nematollahi +3Reinforcement Learning Representation Learning for RL
Department of Computer Science, University of Freiburg, Germany
Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading
Mar 20, 2026Nabeel Ahmad SaiddReinforcement Learning Reward Design for RL
Dr APJ Abdul Kalam Technical University, India
ContractionPPO: Certified Reinforcement Learning via Differentiable Contraction Layers
Mar 20, 2026Vrushabh Zinage, Narek Harutyunyan, Eric Verheyden +2Robust RL Reinforcement Learning
California Institute of Technology · Brown University
DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
Mar 20, 2026Yaqi Xie, Xinru Hao, Jiaxi Liu +4Inventory Control Reinforcement Learning
Booth School of Business, University of Chicago, Chicago, USA. · Taobao & Tmall Group, Hangzhou, China. · School of Economics, Sichuan University, Chengdu, China. +2
Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL
Mar 19, 2026Chenlu Ye, Xuanchang Zhang, Yifan Hao +6Importance Sampling for RL RL for Language Models
University of Illinois Urbana-Champaign · Amazon
Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation
Mar 19, 2026Zhuolin Yang, Zihan Liu, Yang Chen +14RL for Language Model Reasoning RL for LLM Agents
Are complicated loss functions necessary for teaching LLMs to reason?
Mar 19, 2026Gabriele Carrino, Andrea Sassella, Nicolo Brunello +2RL for Language Model Reasoning Group Relative Policy Optimization
DEIB, Politecnico di Milano Via Ponzio 34/5, 20133, Milano (MI), Italy
Evaluating Model-Free Policy Optimization in Masked-Action Environments via an Exact Blackjack Oracle
Mar 19, 2026Kevin SongPolicy Optimization Reinforcement Learning
Department of Biomedical Engineering The University of Alabama at Birmingham
iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery
Mar 19, 2026Jiangtao Luo, Bingbing Xu, Shaohua Xia +1LEO Satellite Networks Edge Computing
the School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China.
Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
Mar 19, 2026Haechan Kim, Soohyun Ryu, Gyouk Chu +2Reinforcement Learning RL for Language Model Reasoning
Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea · KRAFTON, Seoul, South Korea · AITRICS, Seoul, South Korea

Mar 26, 2026J/K move · Enter open · S save

Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model

Ziyan Wang, Peng Chen, Ding Li, Chiwei Li+3

State Key Laboratory of Intelligent Transportation System, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China. · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

Autonomous Driving Simulation Reinforcement Learning RL Exploration Multi-Agent Reinforcement Learning

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Learning diverse and high-fidelity traffic simulations from human driving demonstrations is crucial for autonomous driving evaluation. The recent next-token prediction (NTP) paradigm, widely adopted in large language models (LLMs), has been applied to traffic simulation and achieves iterative improvements via supervised fine-tuning (SFT). However, such methods limit active exploration of potentially valuable motion tokens, particularly in suboptimal regions. Entropy patterns provide a promising perspective for enabling exploration driven by motion token uncertainty. Motivated by this insight, we propose a novel tokenized traffic simulation policy, R1Sim, which represents an initial attempt to explore reinforcement learning based on motion token entropy patterns, and systematically analyzes the impact of different motion tokens on simulation outcomes. Specifically, we introduce an entropy-guided adaptive sampling mechanism that focuses on previously overlooked motion tokens with high uncertainty yet high potential. We further optimize motion behaviors using Group Relative Policy Optimization (GRPO), guided by a safety-aware reward design. Overall, these components enable a balanced exploration-exploitation trade-off through diverse high-uncertainty sampling and group-wise comparative estimation, resulting in realistic, safe, and diverse multi-agent behaviors. Extensive experiments on the Waymo Sim Agent benchmark demonstrate that R1Sim achieves competitive performance compared to state-of-the-art methods.