Reinforcement Learning

Also known as RL

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144 papers in the last four weeks, up 243% on the four weeks before. 1.4% of all new papers.

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Latest papers 1,138

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Provably Safe Sim-to-Real Transfer
Sep 1, 2026Tingting Ni, Maryam KamgarpourReinforcement Learning Sim-to-Real Transfer
SYCAMORE, EPFL
CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs
Sep 1, 2026Chaohui Guo, Michel Klein, Zhisheng HuangReinforcement Learning LLM Inference Scheduling
Department of Computer Science, Vrije Universiteit Amsterdam, Netherlands
Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities
Sep 1, 2026Pablo Benalcazar, Maciej Kalka, Wilian Guamán +1Reinforcement Learning Dynamic Pricing
Division of Energy Economics, Mineral and Energy Economy Research Institute, Polish Academy of Sciences, Kraków, Poland · GITEA, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, Ecuador
Dense Process Supervision for Search Agents via Fact Utility Estimation
Sep 1, 2026Rongzhi Zhu, Xiangyu Liu, Yi Liu +7Reinforcement Learning Credit Assignment in RL
State Key Laboratory for Novel Software Technology, Nanjing University, China · Ant Group, China · National Institute of Healthcare Data Science, Nanjing University, China
It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning
Sep 1, 2026Runpeng Dai, Kaili Huang, Changsung Kang +1Reinforcement Learning Generative Retrieval
University of North Carolina at Chapel Hill · Apple
Independent Reinforcement Learning in Discounted Markov Games
Sep 1, 2026Asrin Efe Yorulmaz, Ugur Aydin, Tamer BasarReinforcement Learning Game Theory
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost
Aug 31, 2026Zihang Liang, Haochen Zhang, Lingzhou XueReinforcement Learning Federated RL
Department of Statistics, The Pennsylvania State University
Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence
Aug 31, 2026Zhiqin Yang, Jingwen Fu, Yuhan Liu +16Reinforcement Learning RL for Language Model Reasoning
The Hong Kong University of Science and Technology · Zhongguancun Academy · Xi’an Jiaotong University +6
Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL
Aug 31, 2026Michal Korniak, Kamil Dybek, Benjamin Eysenbach +2Reinforcement Learning Offline RL
ETH Zurich · University of Warsaw · Princeton University +3
Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic
Aug 31, 2026Olivier Serris, Stéphane Doncieux, Olivier SigaudReinforcement Learning Sparse-Reward RL
ISIR · Sorbonne Université, CNRS
BCPPO: Bachelier-Inspired Constrained Proximal Policy Optimization for Tail-Risk-Aware Safe Reinforcement Learning
Aug 31, 2026Dongsheng Hou, Yanqiao Chen, Yuhan RuiReinforcement Learning Constrained RL
Southern University of Science and Technology
Reinforcement Learning for Symbolic Equation Solving
Aug 31, 2026Kevin P O KeeffeReinforcement Learning Neurosymbolic Learning
Starling Research Institute
Uncertainty-Driven Replay Memory for Reinforcement Learning
Aug 30, 2026Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell +1Reinforcement Learning Generalization in RL
Rochester Institute of Technology
SmoothRL: Online Reinforcement Learning During Asynchronous Execution
Aug 30, 2026Guang Gao, Yuxuan Nong, Baifu Huang +1Robot Policy Learning Asynchronous RL
Astribot Team
RIPE++: Reinforced Keypoint Learning from Positive Pairs Only
Aug 20, 2026Johannes Künzel, Peter Eisert, Anna HilsmannRepresentation Learning Reinforcement Learning
Fraunhofer Heinrich-Hertz-Institute, HHI, Germany · Humboldt University Berlin, Germany
RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
Aug 19, 2026Yugu Li, Zehong Cao, Jianglin Qiao +1Agentic RL Reinforcement Learning
School of CSIT · ACFR · School of EECMS
OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways
Aug 13, 2026Mao Jiayang, Wang Lanfeng, Peng Zhao-HanReinforcement Learning Mobile Robotics
College of Information Engineering Sichuan Agricultural University Yaan, China · Shenzhen International Graduate School Tsinghua University Shenzhen, China
Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
Aug 13, 2026Zijie Cheng, Yang Peng, Zhihua ZhangDistributional RL Reinforcement Learning
School of Mathematical Sciences, Peking University · Yau Mathematical Sciences Center, Tsinghua University
A Deep RL based Framework for Targeted White Matter Tractography
Aug 13, 2026Ankita JoshiReinforcement Learning Magnetic Resonance Imaging
SCHOOL OF COMPUTING AND ELECTRICAL ENGINEERING, INDIAN INSTITUTE OF TECHNOLOGY MANDI · Indian Institute of Technology Mandi, 175005 - INDIA
Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection
Aug 13, 2026Pu Li, Tao Tan, Hong Xie +2Reinforcement Learning Deep Q-Learning
Chongqing Institute of Green and Intelligent Technology, Chongqing, China · University of Science and Technology of China, Hefei, Anhui, China
Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Aug 13, 2026Larissa Xu, King Bi, William ChangRegret Minimization in RL Reinforcement Learning
Department of Mathematics University of California, Los Angeles Los Angeles, USA · Department of Computer Science University of California, Los Angeles Los Angeles, USA
CoAdapt-GUI: Joint Workflow Context and Policy Adaptation for Unseen GUI Applications
Aug 12, 2026Linqiang Guo, Li Gu, Zihuan Jiang +8Reinforcement Learning RL for GUI Agents
Peter · Concordia University · Mila – Québec AI Institute +2
Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving
Aug 11, 2026Aditya Humnabadkar, Huaizhong Zhang, Ardhendu BeheraReinforcement Learning Autonomous Driving Safety Evaluation
Department of Computer Sciences, Edge Hill University, United Kingdom
PAIR: Pairwise-Aware Inclusion Reweighting for Adaptive Rollout Allocation in RLVR
Aug 11, 2026Pixel Nomand, Elena Voss, Marcus Hale +1Inverse Probability Weighting Reinforcement Learning
University of Wisconsin–Madison · University of Washington
Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings
Aug 11, 2026Tran Le VuReinforcement Learning Building Energy Management
Efficient Hypergradient Descent for Inverse Reinforcement Learning
Aug 11, 2026Nikita Sevriukov, Anna Barabanova, Uliana Gagarina +4Reinforcement Learning Gradient Descent
HSE University Moscow, Russia
IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning
Aug 11, 2026Zefeng Liang, Jie Qiao, Ruichu Cai +2Reinforcement Learning Robust RL
School of Computer Science, Guangdong University of Technology, Guangzhou, China · College of Science, Shantou University, Shantou, China
Reinforcement Learning-Based Laser Cutting Machine Parameter Optimization
Aug 11, 2026Khanh Quan Pham, Majid Kundroo, Geunwoo Ban +2Manufacturing Reinforcement Learning
School of Information and Communication Engineering, Chungbuk National University, Cheongju, 28644, Korea. · NPS CO., LTD., Cheongju, 28371, Korea.
Critic-Free Pretraining for Efficient Online Reinforcement Learning Fine-Tuning
Aug 11, 2026Daoyi Li, Yixian Zhang, Wenbo Ding +2Reinforcement Learning Critic-Free RL
Tsinghua University
TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling
Aug 11, 2026Yanyu Ren, Xizheng Wang, Xiao Liu +8Agentic RL Asynchronous RL
Tsinghua University · Zhongguancun Laboratory

Sep 1, 2026, cs.LGJ/K move · Enter open · S save

Provably Safe Sim-to-Real Transfer

Tingting Ni, Maryam Kamgarpour

SYCAMORE, EPFL

Reinforcement Learning Sim-to-Real Transfer Reward-Free RL Safe RL Sample-Efficient RL

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To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where samples are cheap, and then deploy the learned policy in the real world with the hope that it generalizes effectively. Such direct sim-to-real transfer is not guaranteed to succeed: simulator-trained policies can be suboptimal in the real world due to sim-to-real mismatch. Correcting this mismatch requires collecting data from the real system, but in many applications, such as robotics and healthcare, this data-collection process is itself subject to safety constraints. This gives rise to the problem of safe sim-to-real transfer: how can an agent exploit an imperfect simulator while ensuring safe real-world data collection and learning a near-optimal feasible policy for the target system? We address this problem by formulating safe sim-to-real transfer within the framework of reward-free safe RL. We design a computationally efficient algorithm that exploits simulator information to provably reduce real-world interaction while ensuring safe exploration and enabling the computation of a near-optimal feasible policy for any potential reward function. Our real-world sample complexity bound characterizes the benefit of using the simulator in terms of the sim-to-real mismatch.

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Latest papers 1,138