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.

Multi-Objective Constraint Inference using Inverse reinforcement learning
May 7, 2026Syed Ihtesham Hussain Shah, Floris den Hengst, Aneta Lisowska +1Reinforcement Learning Constrained RL
Faculty of Sciences Vrije Universiteit Amsterdam
On the Divergence of Differential Temporal Difference Learning without Local Clocks
May 7, 2026David Antrobius, Shangtong ZhangReinforcement Learning Average-Reward RL
Department of Computer Science University of Virginia
A Sharp Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
May 7, 2026Ege C. Kaya, Abolfazl HashemiDistributional RL Reinforcement Learning
Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47906
SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents
May 7, 2026Xiwen Chen, Wenhui Zhu, Songzhu Zheng +3Reinforcement Learning Credit Assignment in RL
Morgan Stanley · Arizona State University · Columbia University +1
Beyond Negative Rollouts: Positive-Only Policy Optimization with Implicit Negative Gradients
May 7, 2026Mingwei Xu, Hao FangReinforcement Learning RL for Language Model Reasoning
University of Washington, Seattle, USA
Revisiting Adam for Streaming Reinforcement Learning
May 7, 2026Florin Gogianu, Adrian Catalin Lutu, Razvan PascanuDistributional RL Reinforcement Learning
Bitdefender, Bucharest, Romania · Mila, Montreal, Canada
Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
May 7, 2026Michal Kobiela, Diego A. Oyarzún, Michael U. GutmannReinforcement Learning Sequential Experimental Design
School of Informatics, The University of Edinburgh, UK · School of Informatics, School of Biological Sciences, The University of Edinburgh, UK · School of Informatics, The University of Edinburgh,, UK
Market-Alignment Risk in Pricing Agents: Trace Diagnostics and Trace-Prior RL under Hidden Competitor State
May 7, 2026Peiying Zhu, Sidi ChangReward Hacking Reinforcement Learning
Blossom AI · Blossom AI Labs
On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
May 7, 2026Hao Ye, Jisheng Dang, Junfeng Fang +7Reward Over-Optimization Reinforcement Learning
Lanzhou University · National University of Singapore · Beijing Institute of Technology
Operator-Guided Invariance Learning for Continuous Reinforcement Learning
May 7, 2026Zuyuan Zhang, Fei Xu Yu, Tian LanReinforcement Learning Continuous-Time RL
The George Washington University
The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents
May 7, 2026Federico Pigozzi, Michael LevinReinforcement Learning
Allen Discovery Center, Tufts University, USA · Wyss Institute for Biologically Inspired Engineering, Harvard University, USA
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
May 7, 2026Philip Jordan, Maryam KamgarpourNash Equilibrium Computation Reinforcement Learning
SYCAMORE, EPFL
Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under $τ$ -Mixing
May 7, 2026Leon Halgryn, Sophie Langer, Janusz M. Meylahn +1Reinforcement Learning Deep Q-Learning
University of Twente · Department of Applied Mathematics University of Twente · Ruhr-Universität Bochum +1
On Training in Imagination
May 7, 2026Nadav Timor, Ravid Shwartz-Ziv, Micah Goldblum +2Reward Modeling Reinforcement Learning
Weizmann Institute of Science · New York University · Columbia University
AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
May 7, 2026Yaomin Wang, Jianting Pan, Ran Tian +4Reinforcement Learning Temporal-Difference Learning
School of Data Science, The Chinese University of Hong Kong, Shenzhen
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
May 7, 2026Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan +1Reinforcement Learning Agent Skill Learning
Helmholtz Munich, Germany · Max Planck Institute for Biological Cybernetics, Germany · Princeton University, USA +1
ASTOR: Multi-Task Code Reinforcement Learning via Utility-Driven Coordination
May 7, 2026Yujia Chen, Yang Ye, Xiao Chu +2RL for Code Generation Reinforcement Learning
Harbin Institute of Technology, Shenzhen · Huawei Cloud Computing Technologies Co., Ltd.
Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer
May 7, 2026Yongyi Wang, Hanyu Liu, Lingfeng Li +6Efficient Transformer Inference Reinforcement Learning
School of Computer Science Peking University Beijing, China 100871
Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
May 7, 2026Cristiano da Costa Cunha, Ajmal Mian, Tim French +1RL Benchmarks Structural Causal Models
Department of Computer Science and Software Engineering, University of Western Australia, 6009 WA Australia
Measuring Learning Progress via Gradient-Momentum Coupling
May 7, 2026Samuel Blad, Martin Längkvist, Amy LoutfiReinforcement Learning Intrinsic Motivation
Örebro University
Selective Rollout: Mid-Trajectory Termination for Multi-Sample Agent RL
May 7, 2026Zhiyuan Zhai, Xin WangReinforcement Learning Group Relative Policy Optimization
Fudan University
Reward Shaping and Action Masking for Compositional Tasks using Behavior Trees and LLMs
May 7, 2026Nicholas Potteiger, Ankita Samaddar, Taylor T. Johnson +1Reinforcement Learning Reward Shaping
Vanderbilt University, Nashville, TN
Fitted Q-Iteration with Continuous Actions and Adaptive Data: Smoothed Coverage and Minimax Rates
May 7, 2026Manuel Haußmann, Mustafa Mert Çelikok, Melih KandemirReinforcement Learning Q-Learning
Department of Mathematics and Computer Science University of Southern Denmark
Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
May 7, 2026Haodong Liang, Lifeng LaiReinforcement Learning Transformer-Based RL
Department of Electrical and Computer Engineering, University of California, Davis
LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
May 6, 2026Mahyar Alinejad, Yue Wang, Amrit Singh Bedi +1Reinforcement Learning LLM-Guided 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
Hidden States as Value Gradients: The Pontryagin Structure of Recurrent Policies
May 6, 2026David Leeftink, Max Hinne, Marcel van GervenRL Control Reinforcement Learning
Department of Machine Learning and Neural Computating Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, the Netherlands.
Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime
May 6, 2026Tianshu Zhu, Wenyu Zhang, Xiaoying Zuo +8RL for Code Generation Reinforcement Learning
Baidu
LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts
May 6, 2026Seungeun Rho, Shamel Fahmi, Jeonghwan Kim +3Reinforcement Learning Robot Skill Learning
1RAI Institute, Cambridge, MA, USA · Georgia Institute of Technology, Atlanta, GA, USA
Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers
May 6, 2026Senkang Hu, Yong Dai, Xudong Han +4Agentic RL Reinforcement Learning
Hong Kong JC STEM Lab of Smart City · City University of Hong Kong · University of Oxford +4
Modular Reinforcement Learning For Cooperative Swarms
May 6, 2026Erel Shtossel, Gal A. KaminkaMulti-Robot Systems Reinforcement Learning
Bar Ilan University Ramat Gan, Israel

May 7, 2026J/K move · Enter open · S save

Multi-Objective Constraint Inference using Inverse reinforcement learning

Syed Ihtesham Hussain Shah, Floris den Hengst, Aneta Lisowska, Annette ten Teije

Faculty of Sciences Vrije Universiteit Amsterdam

Reinforcement Learning Constrained RL Preference-Based RL

Open paper AskPDF arXiv

Constraint inference is widely considered essential to align reinforcement learning agents with safety boundaries and operational guidelines by observing expert demonstrations. However, existing approaches typically assume homogeneous demonstrations (i.e., generated by a single expert or multiple experts with identical objectives). They also have limited ability to capture individual preferences and often suffer from computational inefficiencies. In this paper, we introduce Multi-Objective Constraint Inference (MOCI), a novel framework designed to jointly extract shared constraints and individual preferences from heterogeneous expert trajectories, where multiple experts pursue different objectives. MOCI effectively models and learns from diverse, and potentially conflicting, behaviors. Empirical evaluations demonstrate that MOCI significantly outperforms existing baselines, achieving improved predictive performance, and maintaining competitive computational efficiency on a standard grid-world benchmark. These results establish MOCI as an accurate, flexible, and computationally practical approach for real-world constraint inference and preference learning tasks.