RL Exploration

RL: Reinforcement Learning

Follow this topic Ask about this topic

Momentum

17 papers in the last four weeks, up 325% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 139

All topics

Information-Based Exploration via Random Features for Reinforcement Learning
Jul 20, 2026Waris Radji, Odalric-Ambrym MaillardRL Exploration
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRIStAL, F-59000 Lille, France
Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning
Jul 19, 2026Joseph Lazzaro, Alessio Russo, Aldo PacchianoMarkov Decision Processes RL Exploration
Imperial College London · Boston University · Broad Institute of MIT and Harvard
When Can Safe Controllers Adapt? Information before Commitment
Jul 18, 2026Venkatesh SaligramaRL Exploration Optimal Control
Department of Electrical and Computer Engineering, Boston University, MA 02215
Principled Direction-Free Intrinsic Motivation through Model-Free Epistemic Free-Energy Estimators
Jul 18, 2026Alireza Furutanpey, Schahram DustdarFree Energy Calculation Intrinsic Reward Methods
Coovally, Barcelona, Spain · Distributed Systems Group, TU Vienna, Austria · ICREA Barcelona, Spain
Process Reward Informed Tree Rollout for Effective Multi-Turn RL
Jul 17, 2026Xintong Li, Sha Li, Yuwei Zhang +8Reinforcement Learning RL Exploration
1UC San Diego · 2Amazon · 3MIT Alumni
ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning
Jul 14, 2026Yilun Kong, Yunpeng Qing, Guozheng Ma +4RL Exploration Robotic Manipulation
Nanyang Technological University · ACE Robotics · Zhejiang University +1
Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization
Jul 11, 2026Zhicheng Cai, Xinyuan Guo, Hanlin Wu +3Reinforcement Learning RL for Language Model Reasoning
Institute for AI Industry Research (AIR), Tsinghua University · ByteDance Seed · SIA-Lab of Tsinghua AIR and ByteDance Seed.
TopoExplore: Topological Discrimination for Archive-Based Exploration
Jul 10, 2026Jason CarlsonRL Exploration Frontier-Based Exploration
Prompt-Driven Exploration
Jul 9, 2026Sunshine Jiang, John Marangola, David Zhang +6LLM-Guided RL RL Exploration
Massachusetts Institute of Technology · Improbable AI Lab · MIT-IBM Computing Research Lab
UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma
Jul 8, 2026Chongyu Fan, Pengfei Liu, Jingjia Huang +2RL for Language Model Reasoning Policy Optimization
ByteDance Seed · Michigan State University
TREK: Distill to Explore, Reinforce to Refine
Jul 6, 2026Yuanda Xu, Zhengze Zhou, Kayhan Behdin +10RL for Language Model Reasoning LLM-Guided RL
LinkedIn Corporation · Harvard University · Georgia Institute of Technology
Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
Jul 5, 2026Fan Feng, Yujia Zheng, Minghao Fu +5World Model Learning World Models
1UCSD · 2MBZUAI · 3CMU +1
Exploration and Online Transfer with Behavioral Foundation Models
Jun 29, 2026Louis Bagot, Mathieu Lefort, Laëtitia MatignonReinforcement Learning RL Exploration
SyCoSMA · Université Lyon 1, CNRS, INSA Lyon LIRIS - UMR5205, 69622 Villeurbanne, France · LIRIS, SyCoSMA, IRISA, MALT, UR +1
Dual-Flow Reinforcement Learning with State-Aware Exploration
Jun 29, 2026Qijun Li, Zheng Fu, Qi Song +4Distributional RL Reinforcement Learning
School of Vehicle and Mobility and the State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration
Jun 24, 2026Ruyi Tang, Grégoire Sergeant-Perthuis, David ColliauxDynamical Systems Reinforcement Learning
LCQB-AG · 1CQSB, Sorbonne University, France · 2Sony CSL, France
Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
Jun 20, 2026Zhao Yang, Yuxuan Jiang, Ting-Chih Chen +18RL for Language Models Credit Assignment in RL
VU Amsterdam, The Netherlands · University of Maryland, Baltimore County, USA · Leiden University, The Netherlands +4
Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
Jun 19, 2026Marco Pratticò, Pietro Novelli, Massimiliano Pontil +1Reinforcement Learning Exploration-Exploitation Tradeoff
Computational Statistics and Machine Learning - Istituto Italiano di Tecnologia, 16100 Genova, Italy · 2AI Centre, Computer Science Department, University College London, London, UK.
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
Jun 18, 2026Asaf Cassel, Aviv RosenbergRegret Minimization in RL Reinforcement Learning
Google Research
Reward as An Agent for Embodied World Models
Jun 18, 2026Pu Li, Zhigang Lin, Qiang Wu +3Reward Modeling Reward Hacking
Kairos Team
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
Jun 17, 2026Calvin Luo, Chen Sun, Shuran SongRobot Policy Adaptation Robotic RL
1Stanford University · 2Brown University.
Can In-Context Learning Support Intrinsic Curiosity?
Jun 17, 2026Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5Intrinsic Reward Methods Intrinsic Motivation
1Google – Paradigms of Intelligence Team, 2Google DeepMind
UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
Jun 17, 2026Mohamed Nabail, Leo Kaixuan Cheng, Jingmin Wang +1Pairwise Preference Evaluation RL Exploration
Learning, Embodied Autonomy, and Forecasting (LEAF) Lab, University of Toronto
Infant Spontaneous Movement Noise Improves Exploration in Deep RL
Jun 15, 2026Francisco M. López, Markus R. Ernst, Francisco Cruz +2Reinforcement Learning RL Exploration
Frankfurt Institute for Advanced Studies, Germany, and with the School of Computer Science and Engineering, University of New South Wales, Australia · School of Computer Science and Engineering, University of New South Wales, Australia · School of Computer Science and Engineering, University of New South Wales, Australia, and with Escuela de Ingenier´ıa, Universidad Central de Chile, Chile +2
Repeated Bilateral Trade: The Quest for Fairness
Jun 13, 2026François Bachoc, Roberto Colomboni, Emilie KaufmannMechanism Design Algorithmic Fairness
University of Lille Institut Universitaire de France (IUF) Lille, France · School of Mathematics University of Bristol Fry Building, Woodland Road Bristol BS8 1UG, United Kingdom · Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189-CRIStAL F-59000 Lille, France
Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Starts
Jun 13, 2026Octave Oliviers, Glenn VinnicombeRL Control Reinforcement Learning
Department of Engineering · University of Cambridge · Cambridge, UK
VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
Jun 12, 2026Venkata Naren Devarakonda, Raktim Gautam Goswami, Prashanth Krishnamurthy +1Diffusion-Based Planning Robot Navigation
Control/Robotics Research Laboratory (CRRL), Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, 11201.
Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning
Jun 9, 2026Jiangnan Xia, Yucheng Shi, Yu Yang +3Memorization in Language Models RL for Language Model Reasoning
University of Georgia · Tencent AI Lab · The Education University of Hong Kong +1
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
Jun 8, 2026Kaustubh Mani, Yann Pequignot, Vincent Mai +1Sharpness-Aware Minimization RL Exploration
Universit´e de Montr´eal · Mila - Qu´ebec AI Institute · Universit´e Laval +2
An Information-Theoretic Definition for Open-Ended Learning
Jun 6, 2026Wanqiao Xu, Yifan Zhu, Benjamin Van RoyLifelong Learning Agents RL Exploration
Retry Policy Gradients in Continuous Action Spaces
Jun 4, 2026Soichiro Nishimori, Paavo ParmasPolicy Optimization RL Exploration
The University of Tokyo, Japan

Jul 20, 2026, cs.LGJ/K move · Enter open · S save

Information-Based Exploration via Random Features for Reinforcement Learning

Waris Radji, Odalric-Ambrym Maillard

Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRIStAL, F-59000 Lille, France

RL Exploration

Open paper AskPDF arXiv

Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish. We introduce Random Feature Information Gain (RFIG), grounded in Bayesian kernel methods theory, which uses random Fourier features to approximate information gain and compute exploration bonuses in non-countable spaces. We provide error bounds on information gain approximation and avoid the black-box aspects of neural network-based uncertainty estimation, for optimism-based exploration. We present practical details that make RFIG scalable to deep RL scenarios, enabling smooth integration into standard deep RL algorithms. Experimental evaluation across diverse control and navigation tasks demonstrates that RFIG achieves competitive performance with well-established deep exploration methods while offering superior theoretical interpretation.

Momentum

Related topics

Latest papers 139