Regularized RL

RL: Reinforcement Learning

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24 papers6 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Recommender regret for improving regularised RL estimation with bandits and MCTS
Oct 23, 2026Danilo Vucetic, Alexandre Larouche, Mohammad Pedramfar +2Regularized RL Reinforcement Learning
Université de Montréal · Mila - Québec AI Institute · Université Laval +2
Regularised RL improves sim-to-real transfer
Oct 23, 2026Danilo Vucetic, Mohamad H. Danesh, Florence Clerc +2Regularized RL Reinforcement Learning
Université de Montréal · Mila - Québec AI Institute · McGill University +2
Mixing Regularization for Average-Reward $Q$ -Learning and $Q$ -Planning
Oct 10, 2026Julien Roger, Sam Bodmer, Jian Qian +1Regularized RL Reinforcement Learning
École polytechnique · New York University · The University of Hong Kong
Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning
Oct 6, 2026SungJae Ahn, Jeong Woon Lee, Kyoleen Kwak +1RL Control Reinforcement Learning
College of Software, Kyung Hee University.
FERPO: Forward Entropy-Regularized Policy Optimization
Oct 1, 2026Sebastian Sanokowski, Alireza Sarmadi, Majid KhadivKL-Regularized RL Policy Optimization
Applied and Theoretical Aspects of Robot Intelligence (ATARI) Lab, Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich
Provable Benefits of Regularization: Fast Rates for Adversarial Imitation Learning
Sep 28, 2026Hanbin Zhou, Shangzhe Li, Alexander Braverman +1Reinforcement Learning Adversarial Imitation Learning
HKUST(GZ) · UNC Chapel Hill · Cornell University
A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Sep 11, 2026Amine Andam, Jamal Bentahar, Mustapha HedabouKL-Regularized RL Adversarial Robustness
Mohammed VI Polytechnic University · Khalifa University · Concordia University
SCQ: Stabilizing Conservative Q-Learning with Sigmoid-Bounded Entropy
Sep 11, 2026Xiefeng Wu, Shu Zhang, Zhaojie Chu +1Offline-To-Online RL Conservative RL
School of Computer Science, Wuhan University, Wuhan, China · School of Electronic Information, Wuhan University, Wuhan, China · School of Internet, Anhui University, Hefei, China
ReBRAC-v2: The Return of the King
Aug 2, 2026Denis Tarasov, Robert K. KatzschmannReinforcement Learning Offline RL
Soft Robotics Lab, D-MAVT ETH Zurich, Switzerland
PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers
Jul 10, 2026Yujie Pang, Zudong LiReinforcement Learning Contact-Rich Robotic Manipulation
Dept. of Mechanical and Energy Engineering · Southern University of Science and Technology · Shenzhen, China
Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
Jul 3, 2026Jialun Cao, Fernando Acero, David Šiška +1Robust Markov Decision Processes Maximum Entropy RL
School of Mathematics, University of Edinburgh, United Kingdom. · J.P. Morgan AI Research, United Kingdom. · Department of Mathematics, Imperial College London, United Kingdom.
Regularized Reward-Punishment Reinforcement Learning
Jun 26, 2026Jiexin Wang, Eiji UchibeReinforcement Learning KL-Regularized RL
Dept. of Brain Robot Interface, ATR Computational Neuroscience Laboratories, 2-2-2, Hikaridai, Seikacho, Soraku-gun, Kyoto, 619-0288, Japan
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Jun 4, 2026Haoyang Hong, Zichen Wang, Quanquan Gu +1Regret Minimization in RL Reinforcement Learning
School of Electrical Engineering and Computer Science, Oregon State University · Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois Urbana-Champaign · Department of Computer Science, University of California, Los Angeles
Ratio-Variance Regularized Policy Optimization
May 26, 2026Yu Luo, Shuo Han, Yihan Hu +5RL for Language Model Reasoning Policy Optimization
Department of Foundation Model, 2012 Labs, Huawei Shanghai · Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University · Department of Computer Science and Technology, Tsinghua University +1
Refined Analysis of Entropy-Regularized Actor-Critic
May 23, 2026Safwan Labbi, Paul Mangold, Daniil Tiapkin +1Reinforcement Learning Maximum Entropy RL
CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France · Université Paris-Saclay, CNRS, LMO, 91405, Orsay, France · Google DeepMind +2
Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
May 9, 2026Qingyue Zhao, Kaixuan Ji, Heyang Zhao +1Offline-to-Online Contextual Bandits KL-Regularized RL
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Vanishing L2 regularization for the softmax Multi Armed Bandit
May 5, 2026Stefana-Lucia Anita, Gabriel TuriniciMulti-Armed Bandits Policy Gradient
CEREMADE, Universit´e Paris Dauphine - PSL, CNRS, Paris, France · ‘Octav Mayer” Institute of Mathematics of the Romanian Academy, Bd. Carol I 8, Ias¸i 700505, Romania
Planning in entropy-regularized Markov decision processes and games
Apr 21, 2026Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard +2Value Function Estimation Markov Models
DeepMind Paris · SequeL team, Inria Lille
Reinforcement Learning via Value Gradient Flow
Apr 15, 2026Haoran Xu, Kaiwen Hu, Somayeh Sojoudi +1Regularized RL Reinforcement Learning
University of Texas at Austin · University of California, Berkeley
Value Mirror Descent for Reinforcement Learning
Apr 7, 2026Zhichao Jia, Guanghui LanMarkov Decision Processes Reinforcement Learning
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332.
DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
Mar 20, 2026Yaqi Xie, Xinru Hao, Jiaxi Liu +4Inventory Control Reinforcement Learning
Booth School of Business, University of Chicago, Chicago, USA. · Taobao & Tmall Group, Hangzhou, China. · School of Economics, Sichuan University, Chengdu, China. +2
Optimistic Policy Regularization: Elite-Statistic Reward Shaping
Mar 6, 2026Mai Pham, Vikrant Vaze, Peter ChinRegularized RL Sample-Efficient RL
Thayer School of Engineering, Dartmouth College, USA
Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
Feb 20, 2026Johannes Ackermann, Michael Noukhovitch, Takashi Ishida +1Reward Hacking Reinforcement Learning with Verifiable Rewards
The University of Tokyo · Mila · RIKEN AIP
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Oct 11, 2023Zeyang Li, Chuxiong Hu, Yunan Wang +4Markov Decision Processes Reinforcement Learning
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, United States · Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China · College of Artificial Intelligence and the School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

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Recommender regret for improving regularised RL estimation with bandits and MCTS

Danilo Vucetic, Alexandre Larouche, Mohammad Pedramfar, Audrey Durand+1

Université de Montréal · Mila - Québec AI Institute · Université Laval+2

Regularized RL Reinforcement Learning Monte Carlo Tree Search Regret Minimization in RL Multi-Armed Bandits

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Regularised reinforcement learning (RL) algorithms rely on estimators, like Monte Carlo tree search or one-step look-ahead, to provide policy, value, or Q-value estimates in training and inference. However, existing bandit-based estimation frameworks evaluate sampling efficiency, not the quality of estimates. We address this by formalising estimate generation as a recommendation process, treating bandit-based sampling as an improvement operator. To evaluate the quality of a recommendation (estimate), we define regulariser-aware recommender regrets, analysing their lower bounds, and proving that simple algorithms like uniform sampling can achieve these bounds. Finally, we unify bandit- and search-based target improvement under recommender MCTS. Across synthetic trees, arcade games, and scientific discovery tasks, recommenders often yield faster target convergence and superior downstream performance. Our experiments characterise for the first time the potential of regularised MCTS in scientific applications.