Reinforcement Learning

Also known as RL

Overview

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

CardsList

1,742 papers237 papers in the last four weeks, up 46% on the four weeks before. 1.4% of all new papers.

  1. Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

    Mar 6, 2026Miguel Costa, Arthur Vandervoort, Carolin Schmidt +5Reinforcement Learning

  2. Synthetic Monitoring Environments for Reinforcement Learning

    Mar 6, 2026Leonard Pleiss, Carolin Schmidt, Maximilian SchifferRL BenchmarksReinforcement Learning

  3. Stabilizing Reinforcement Learning for Diffusion Language Models

    Mar 6, 2026Jianyuan Zhong, Kaibo Wang, Ding Ding +5RL for Diffusion ModelsReinforcement Learning

  4. Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments

    Mar 6, 2026Michael Beukman, Khimya Khetarpal, Zeyu Zheng +4Reinforcement LearningProximal Policy Optimization

  5. Reinforcement Learning for Power-Flow Network Analysis

    Mar 5, 2026Alperen Ergur, Julia Lindberg, Vinny MillerPower SystemsReinforcement Learning

  6. A Novel Hybrid Heuristic-Reinforcement Learning Optimization Approach for a Class of Railcar Shunting Problems

    Mar 5, 2026Ruonan Zhao, Joseph GeunesRL for Combinatorial OptimizationReinforcement Learning

  7. Wiki-R1: Incentivizing Multimodal Reasoning for Knowledge-based VQA via Data and Sampling Curriculum

    Mar 5, 2026Shan Ning, Longtian Qiu, Xuming HeVisual Question AnsweringReinforcement Learning

  8. Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics

    Mar 5, 2026Kilian Freitag, Knut Åkesson, Morteza Haghir ChehreghaniRL for RoboticsReinforcement Learning

  9. Reward-Conditioned Reinforcement Learning

    Mar 5, 2026Michal Nauman, Marek Cygan, Pieter AbbeelReinforcement LearningOff-Policy RL

  10. What Does Flow Matching Bring To TD Learning?

    Mar 4, 2026Bhavya Agrawalla, Michal Nauman, Aviral KumarReinforcement LearningValue Function Estimation

  11. Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization

    Mar 4, 2026Haodong Zhu, Yangyang Ren, Yanjing Li +6Reinforcement LearningRL for Language Model Reasoning

  12. BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning

    Mar 4, 2026Tarjei Paule Hage, Markus J. BuehlerStructural EngineeringRL for Language Model Reasoning

  13. GIPO: Gaussian Importance Sampling Policy Optimization

    Mar 4, 2026Chengxuan Lu, Zhenquan Zhang, Shukuan Wang +3Reinforcement LearningImportance Sampling for RL

  14. Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling

    Mar 4, 2026Emile Anand, Ishani KarmarkarMean-Field MARLReinforcement Learning

  15. Alternating Reinforcement Learning with Contextual Rubric Rewards: Beyond the Scalarization Strategy

    Mar 4, 2026Guangchen Lan, Lian Xiong, Xin Zhou +7Reinforcement LearningRubric-Based RL

  16. Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm

    Mar 4, 2026Septian Enggar Sukmana, Sang Won Bae, Tomohiro ShibataPrioritized Experience ReplayParkinson's Disease

  17. Q-Measure-Learning for Continuous State RL: Efficient Implementation and Convergence

    Mar 3, 2026Shengbo WangReinforcement LearningQ-Learning

  18. Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning

    Mar 3, 2026Harin Lee, Kevin JamiesonDelayed RLReinforcement Learning

  19. Edit in 2D, Verify in 3D: Reinforcement Learning for Multi-view Consistent Scene Editing

    Mar 3, 2026Jiyuan Wang, Chunyu Lin, Lei Sun +8Multi-View ConsistencyReinforcement Learning

  20. Reinforcement Learning with Symbolic Reward Machines

    Mar 3, 2026Thomas Krug, Daniel NeiderNon-Markovian RLReinforcement Learning

  21. Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

    Mar 3, 2026Ramin Hashemi, Vismika Ranasinghe, Teemu Veijalainen +2Millimeter-Wave CommunicationsWireless Communications

  22. Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving

    Mar 3, 2026Tianze Zhu, Yinuo Wang, Wenjun Zou +6Reinforcement LearningRL for Autonomous Driving

  23. Efficient RLVR Training via Weighted Mutual Information Data Selection

    Mar 2, 2026Xinyu Zhou, Boyu Zhu, Haotian Zhang +2Reinforcement LearningRL for Language Model Reasoning

  24. SEAR: Sample Efficient Action Chunking Reinforcement Learning

    Mar 2, 2026C. F. Maximilian Nagy, Onur Celik, Emiliyan Gospodinov +4Reinforcement LearningSample-Efficient RL

  25. State-Action Inpainting Diffuser for Continuous Control with Delay

    Mar 2, 2026Dongqi Han, Wei Wang, Enze Zhang +1Delayed RLReinforcement Learning

  26. GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control

    Mar 2, 2026Haofeng Xu, Junwei Su, Yukun Tian +3RL StabilityAsynchronous Advantage Actor-Critic