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.

Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control
Mar 26, 2026Zhuoli Zhuang, Yu-Cheng Chang, Yu-Kai Wang +2RL for Autonomous Driving Reinforcement Learning
University of Technology Sydney
Reinforcing Structured Chain-of-Thought for Video Understanding
Mar 26, 2026Peiyao Wang, Haotian Xu, Noranart Vesdapunt +6Structured CoT Reasoning RL for VLMs
Stony Brook University · Amazon
PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning
Mar 26, 2026Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin AlamdariContextual RL Adaptive Sensing
Department of Civil and Environmental Engineering Florida State University, Tallahassee, FL, USA
Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning
Mar 26, 2026Jiajun Hu, Nuria Armengol Urpi, Jin Cheng +1Zero-Shot RL Reinforcement Learning
Department of Mechanical Engineering, EPFL · Department of Computer Science, ETH Zurich · Max Planck Institute for Intelligent Systems
Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
Mar 26, 2026Mutong Liu, Yang Liu, Jiming LiuReinforcement Learning Healthcare
Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
Label What Matters: Modality-Balanced and Difficulty-Aware Multimodal Active Learning
Mar 26, 2026Yuqiao Zeng, Xu Wang, Tengfei Liang +3Active Learning Multimodal Classification
Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education; State Key Laboratory of Advanced Rail Autonomous Operation; School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China · School of Psychology & Neuroscience, University of Glasgow, Glasgow, UK
GDPO-Listener: Expressive Interactive Head Generation via Auto-Regressive Flow Matching and Group reward-Decoupled Policy Optimization
Mar 26, 2026Zhangyu Jin, Maksim Siniukov, Deuksin Kwon +2Human Motion Generation Talking Head Generation
University of Southern California, Institute for Creative Technologies
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

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

Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control

Zhuoli Zhuang, Yu-Cheng Chang, Yu-Kai Wang, Thomas Do+1

University of Technology Sydney

RL for Autonomous Driving Reinforcement Learning RL from Human Feedback Multimodal Reward Modeling

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Recent advancements in computer vision have accelerated the development of autonomous driving. Despite these advancements, training machines to drive in a way that aligns with human expectations remains a significant challenge. Human factors are still essential, as humans possess a sophisticated cognitive system capable of rapidly interpreting scene information and making accurate decisions. Aligning machine with human intent has been explored with Reinforcement Learning with Human Feedback (RLHF). Conventional RLHF methods rely on collecting human preference data by manually ranking generated outputs, which is time-consuming and indirect. In this work, we propose an electroencephalography (EEG)-guided decision-making framework to incorporate human cognitive insights without behaviour response interruption into reinforcement learning (RL) for autonomous driving. We collected EEG signals from 20 participants in a realistic driving simulator and analyzed event-related potentials (ERP) in response to sudden environmental changes. Our proposed framework employs a neural network to predict the strength of ERP based on the cognitive information from visual scene information. Moreover, we explore the integration of such cognitive information into the reward signal of the RL algorithm. Experimental results show that our framework can improve the collision avoidance ability of the RL algorithm, highlighting the potential of neuro-cognitive feedback in enhancing autonomous driving systems. Our project page is: https://alex95gogo.github.io/Cognitive-Reward/.