RLJ Volume 7 (2026)

Pre-proceedings for RLC 2026. Papers may still receive minor changes, and page numbers will be assigned in the final proceedings.

Overcoming Valid Action Suppression in Unmasked Policy Gradient Algorithms

Renos Zabounidis, Roy Siegelmann, Mohamad Qadri, Woojun Kim, Simon Stepputtis, Katia P. Sycara; 7, 2026.
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Centralized Adaptive Sampling for Reliable Co-training of Independent Multi-Agent Policies

Nicholas E. Corrado, Josiah P. Hanna; 7, 2026.
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Yes, Q-learning Helps Offline In-Context RL

Denis Tarasov, Alexander Nikulin, Ilya Zisman, Albina Klepach, Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Igor Kiselev, Vladislav Kurenkov; 7, 2026.
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On the Variance of Temporal Difference Learning and its Reduction Using Control Variates

Hsiao-Ru Pan, Bernhard Schölkopf; 7, 2026.
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Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry

Alexander Fabisch, Melvin Laux, Mariela De Lucas Alvarez, Edoardo Caroselli, Julian Theis; 7, 2026.
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Towards Formalizing Reinforcement Learning Theory: A Robbins-Siegmund Approach

Shangtong Zhang; 7, 2026.
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Statistical Inference for Policy Evaluation with Temporal Difference Learning

Weichen Wu, Gen Li, Yuting Wei, Alessandro Rinaldo; 7, 2026.
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PPO+: Enhancing proximal policy optimization

Mahdi Kallel, Jose-Luis Holgado-Alvarez, Samuele Tosatto, Carlo D'Eramo; 7, 2026.
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Learning When to Stop: Prefix-Optimal Dynamic Diffusion Policies for Continuous Control

Rohit Kumar Salla, Manoj Saravanan, Simon Stepputtis; 7, 2026.
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Limits of reinforcement learning for decision trees in Markov decision processes

Hector Kohler, Riad Akrour, Philippe Preux; 7, 2026.
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Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning

Hsiao-Ru Pan, Bernhard Schölkopf; 7, 2026.
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From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models

Christian Gumbsch, Leonardo Barcellona, Lennard Schuenemann, Platon Karageorgis, Andrii Zadaianchuk, Zehao Wang, Sergey Zakharov, Fabien Despinoy, Rahaf Aljundi, Stratis Gavves; 7, 2026.
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Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Dynamics Models

Julia Berger, Bernd Frauenknecht, Sebastian Trimpe, Bastian Leibe; 7, 2026.
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Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning

Aleksandar Todorov, Matthia Sabatelli; 7, 2026.
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CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning

Marcel Hedman, Kale-ab Tessera, Juan Claude Formanek, Anya Sims, Riccardo Zamboni, Trevor McInroe, John Torr, Elliot Fosong; 7, 2026.
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Repetition as Reinforcement: Enhancing Sample Efficiency via Instant Episode Repetition in Reinforcement Learning

Hoda Yamani, Yuning Xing, Koen van Rijnsoever, Bruce A. MacDonald, Henry Williams; 7, 2026.
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Risk-Aware General-Utility Markov Decision Processes

Pedro Pinto Santos, Fábio Vital, Alberto Sardinha, Francisco S. Melo; 7, 2026.
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The Yokai Learning Environment: Tracking Beliefs Over Space and Time

Constantin Ruhdorfer, Matteo Bortoletto, Johannes Forkel, Jakob Nicolaus Foerster, Andreas Bulling; 7, 2026.
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Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments

Michael Beukman, Khimya Khetarpal, Zeyu Zheng, Will Dabney, Jakob Nicolaus Foerster, Michael D Dennis, Clare Lyle; 7, 2026.
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Stable Planning through Aligned Representations in Model-Based Reinforcement Learning

Misagh Soltani, Forest Agostinelli; 7, 2026.
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Delightful Policy Gradient

Ian Osband; 7, 2026.
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Beyond Local Views: Global State Inference with Diffusion Models for Cooperative MARL

Zhiwei Xu, Hangyu Mao, ZHANG NIANMIN, Shengtao Zhang, Xin Xin, Pengjie Ren, Dapeng Li, Bin Zhang, Guoliang Fan, Zhumin Chen, Changwei Wang, Jiangjin Yin; 7, 2026.
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The Cell Must Go On: Agar.io for Continual Reinforcement Learning

Mohamed Ayman Mohamed, Kateryna Nekhomiazh, Vedant Vyas, Marcos Menon Jose, Andrew Patterson, Marlos C. Machado; 7, 2026.
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Momba: Network Modernization Improves Multi-Objective Reinforcement Learning

Adam Štafa, Santeri Heiskanen, Petr Novotný, Joni Pajarinen; 7, 2026.
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ICPL: Few-shot In-context Preference Learning via LLMs

Chao Yu, Qixin Tan, Hong Lu, Jiaxuan Gao, Xinting Yang, Yu Wang, Yi Wu, Eugene Vinitsky; 7, 2026.
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Natural Policy Gradient for Bayesian Network Policies in Markov Potential Games

Dingyang Chen, Zhenyu Zhang, Yuan Ling, Qi Zhang; 7, 2026.
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Gradient Iterated Temporal-Difference Learning

Théo Vincent, Kevin Gerhardt, Yogesh Tripathi, Habib Maraqten, Adam White, Martha White, Jan Peters, Carlo D'Eramo; 7, 2026.
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Confidence Intervals for the Interquartile Mean

Alexandra Burushkina, Philip S. Thomas; 7, 2026.
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Temporally Extended Mixture-of-Experts Models

Zeyu Shen, Peter Henderson; 7, 2026.
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Synthetic Monitoring Environments for Reinforcement Learning

Leonard S. Pleiss, Carolin Schmidt, Maximilian Schiffer; 7, 2026.
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Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning

Oleg Shchendrigin, Egor Cherepanov, Alexey Kovalev, Aleksandr Panov; 7, 2026.
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PGTG: Procedurally Generated Grid-Based Traffic Gym

Joshua Meyer, Felix Maurice Kuntz, Verena Wolf, Jörg Hoffmann, Timo P. Gros; 7, 2026.
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Extending Differential Temporal Difference Methods for Episodic Problems

Kris De Asis, Mohamed Elsayed, Jiamin He; 7, 2026.
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Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning

Sagalpreet Singh, Rishi Saket, Aravindan Raghuveer; 7, 2026.
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Credit Assignment and Focused Exploration for Sparse-reward Multi-agent Deep Reinforcement Learning

Shuai Han, Mehdi Dastani, Shihan Wang; 7, 2026.
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Multi-Agent Reinforcement Learning with Reward Machines for Mixed Cooperative-Competitive Environments

Sriram Ganapathi Subramanian, Toryn Q. Klassen, Sheila A. McIlraith; 7, 2026.
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StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent

Alex Davey, Alena Shilova, Brahim Driss, Riad Akrour; 7, 2026.
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Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality

Akhil S Anand, Shambhuraj Sawant, Paavo Parmas, Jasper Hoffmann, Dirk Reinhardt, Sebastien Gros; 7, 2026.
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Forager: a lightweight testbed for continual learning with partial observability in RL

Steven Tang, Xinze Xiong, Anna Hakhverdyan, Andrew Patterson, Jacob Adkins, Jiamin He, Esraa Elelimy, Parham Mohammad Panahi, Martha White, Adam White; 7, 2026.
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Near-Optimal Reinforcement Learning for Linear Distributionally Robust Markov Decision Processes

Zhishuai Liu, Weixin Wang, Pan Xu; 7, 2026.
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Revisiting FTA: A Sparse One-to-Many Activation for Reinforcement Learning

Tyler Lazar, Matthew Vandergrift, Martha White, Adam White; 7, 2026.
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Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability

Taewoon Kim, Vincent Francois-Lavet, Michael Cochez; 7, 2026.
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On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents

Oliver Mortensen, M. Sadegh Talebi; 7, 2026.
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Randomized Exploration for Linear Bandits via Absolute Perturbations

Toshinori Kitamura, Shuai Liu, Csaba Szepesvari; 7, 2026.
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Counterfactual Shapley Credit Assignment

Mingxuan Li, Kai-Zhan Lee, Elias Bareinboim; 7, 2026.
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Ludax: A GPU-Accelerated Description Language for Board Games

Graham Todd, Alexander George Padula, Dennis J. N. J. Soemers, Sam Earle, Julian Togelius; 7, 2026.
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Scalable Causal Imitation Learning

Eylam Tagor, Mingxuan Li, Elias Bareinboim; 7, 2026.
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Intrinsic Closed-Loop Practical Asymptotic Stability in Discrete-Time Reinforcement Learning

Jan de Priester, Ricardo Sanfelice; 7, 2026.
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Toward Agents That Reason About Their Computation

Adrian Orenstein, Jessica Chen, Gwyneth Anne Delos Santos, Bayley Sapara, Michael Bowling; 7, 2026.
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Learning the Supports for Categorical Critic in Reinforcement Learning

Jen-Yen Chang, Takayuki Osa, Tatsuya Harada; 7, 2026.
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Modification-Considering Value Learning for Reward Hacking Mitigation in RL

Evgenii Opryshko, Umangi Jain, Igor Gilitschenski; 7, 2026.
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Using Common Random Numbers for Simulation-based Planning with Rollouts

Sandarbh Yadav, Frederic J Maliakkal, Harshad Khadilkar, Shivaram Kalyanakrishnan; 7, 2026.
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Trajectory First: A Curriculum for Discovering Diverse Policies

Cornelius V. Braun, Sayantan Auddy, Marc Toussaint; 7, 2026.
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Rethinking the Suitability of RL Algorithms Under Practical Transfer Constraints

Hany Hamed, Abhishek Naik, Colin Bellinger, A. Rupam Mahmood; 7, 2026.
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Learning Multi-Agent Communication Protocol: Study on Information Entropy Efficiency in MARL

Xinren Zhang, Zixin Zhong, Jiadong Yu; 7, 2026.
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Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes

Asha Barua, Sajad Khodadadian; 7, 2026.
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V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control

Donghu Kim, Youngdo Lee, Hojoon Lee, Johan Obando-Ceron, Byungkun Lee, Aaron Courville, Pablo Samuel Castro, Jaegul Choo, Clare Lyle; 7, 2026.
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Coordination Graphs for Constrained Multi-Agent Reinforcement Learning

Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson; 7, 2026.
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Maximum-Entropy Exploration with Future State-Action Visitation Measures

Adrien Bolland, Gaspard Lambrechts, Damien Ernst; 7, 2026.
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Fully Offline Reinforcement Learning

Mattie Fellows, Clarisse Wibault, Uljad Berdica, Johannes Forkel, Michael A Osborne, Jakob Nicolaus Foerster; 7, 2026.
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Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs

Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan, Wendelin Boehmer; 7, 2026.
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Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function

Qining Zhang, Lei Ying; 7, 2026.
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Conformal Preemption of Failures in Sequential Decision-Making Agents

Garrett Ethan Katz, Adebayo Braimah, Qinru Qiu, Simon Khan; 7, 2026.
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PB²: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning

Brahim Driss, Alex Davey, Riad Akrour; 7, 2026.
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Dynamics Models for Offline Hyperparameter Selection in Real-World RL

Jordan Coblin, Han Wang, Martha White, Adam White; 7, 2026.
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An Agent-Centric Dynamical Systems Perspective on Multi-Agent Reinforcement Learning

James Rudd-Jones, Maria Perez-Ortiz, Mirco Musolesi; 7, 2026.
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Let it Cook: Learning to Wait in Sequential Decision Making

Christopher Watson, Arjun Krishna, Dinesh Jayaraman, Rajeev Alur; 7, 2026.
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Provable Distributional Value Iteration under Partial Observability

Larry Preuett, Qiuyi Zhang, Muhammad Aurangzeb Ahmad; 7, 2026.
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Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See

Mohamed Benabdelouahad, AHMED DJALAL HACINI, Nadir Farhi, Aissa Boulmerka; 7, 2026.
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Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning

Naveen Mysore; 7, 2026.
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Distributionally Robust Self Paced Curriculum Reinforcement Learning

Anirudh Satheesh, Keenan Powell, Vaneet Aggarwal; 7, 2026.
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Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions

Junlin Xiao, Victor-Alexandru Darvariu, Bruno Lacerda, Nick Hawes; 7, 2026.
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ContrastSpanner: Learning Low-Rank Causal Contrasts to Alleviate Power-Set and Eluder Barriers

Alec Koppel, Laixi Shi; 7, 2026.
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DART: Dual Adaptive Residual Tracking for Low-Bias Advantage Estimation and Credit Assignment in AI Agents

Shahrad Mohammadzadeh, Amir-massoud Farahmand, Reihaneh Rabbany, Doina Precup; 7, 2026.
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Goal-Oriented Reinforcement Learning for Stochastic Shortest Paths with Dead-Ends

Gustavo De Mari Pereira, Leliane N. de Barros; 7, 2026.
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ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

Anurag Akula, Satheesh K Perepu, Abhishek Sarkar, Kaushik Dey; 7, 2026.
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Learning with Coupled Uncertainty

Waqar Mirza, Aldo Pacchiano, Eric Mazumdar; 7, 2026.
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NutriRL: A Benchmark for Nutritional Regulation under Delayed State Transitions

Aniket Khan, Charitha Palika, V.Srinivasa Chakravarthy; 7, 2026.
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Representation Regularization in Distributional Reinforcement Learning

André Inge, Jonas Nordqvist, Björn Lindenberg, Karl-Olof Lindahl; 7, 2026.
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Strategically-Linked Decisions in Long-Term Planning and Reinforcement Learning

Alihan Hüyük, Jonas B Raedler, Leo Benac, Finale Doshi-Velez; 7, 2026.
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Reward Design Agent for Reinforcement Learning

Hojoon Lee, Ajay Subramanian, Ben Abbatematteo, Vijay Veerabadran, Pedro Matias, Karl Ridgeway, Nitin Kamra; 7, 2026.
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Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies

Mumuksh Tayal, Manan Tayal, Ravi Prakash; 7, 2026.
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A Causality-Inspired Spatial-Temporal Return Decomposition Approach for Multi-Agent Reinforcement Learning

Yudi Zhang, Yali Du, Biwei Huang, Mykola Pechenizkiy, Meng Fang; 7, 2026.
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Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning

Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, Roberto Martín-Martín; 7, 2026.
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Supervised Reward Inference

Will Schwarzer, Jordan Jack Schneider, Philip S. Thomas, Scott Niekum; 7, 2026.
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Minimal Ingredients for Reward Assignment from Expert Demonstrations

Zixuan Dong, Yumi Omori, Keith W. Ross; 7, 2026.
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Solvable models of learning to pursue a moving target

John J. Vastola, Kanaka Rajan; 7, 2026.
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Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids

Hadi Nekoei, Alexandre Blondin Massé, Rachid Hassani, Sarath Chandar, Vincent Mai; 7, 2026.
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Adaptive Critic Shaping for Reinforcement Learning with Temporal Logic Constraint

Duo XU; 7, 2026.
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Weight a moment: risk-aware exploration with Bayes

Karim Zaghw, Peter Dayan, Georgy Antonov; 7, 2026.
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When Do We Need LLMs? A Diagnostic for Language-Driven Bandits

Uljad Berdica, Fernando Acero, Anton Ipsen, Parisa Zehtabi, Michael Cashmore, Manuela Veloso; 7, 2026.
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Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe; 7, 2026.
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Discovering Reinforcement Learning Interfaces with Large Language Models

Akshat Singh Jaswal, Ashish Baghel, Paras Chopra; 7, 2026.
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When to Plan: Learning to Select Between Reactive Control and Deliberative Planning

Adam Labiosa, Josiah P. Hanna; 7, 2026.
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Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

Jiajun Hu, Núria Armengol Urpí, Jin Cheng, Stelian Coros; 7, 2026.
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An Unreasonably Simple Approach to Safe RL

Geraud Nangue Tasse, Mark Nemecek, Tamlin Love, Steven James, Benjamin Rosman; 7, 2026.
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SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning

Manav Vora, Gokul Puthumanaillam, Hiroyasu Tsukamoto, Melkior Ornik; 7, 2026.
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SegDAC: Visual Generalization in Reinforcement Learning via Dynamic Object Tokens

Alexandre Brown, Glen Berseth; 7, 2026.
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Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification

Haoyang Hong, Zichen Wang, Quanquan Gu, Huazheng Wang; 7, 2026.
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Conservative Value Priors: A Bayesian Path to Offline Reinforcement Learning

Filippo Valdettaro, Yingzhen Li, Aldo A. Faisal; 7, 2026.
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Rationalizing Boltzmann Rationality: An Axiomatic Characterization of Entropy-Regularized Policies

Silviu Pitis; 7, 2026.
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Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots

Khurram Javed, Joseph Varughese Modayil, Gloria Kennickell, Richard S Sutton, John Carmack; 7, 2026.
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Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning

Vincent Taboga, Justin Veilleux, Doseok Jang, Anushree Rankawat, Pierre-Luc Bacon; 7, 2026.
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Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning

Ali Larian, Qian Lin, Chang Zong Wu, Daniel S. Brown; 7, 2026.
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Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL

Dillon Sandhu, Ronald Parr; 7, 2026.
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Hierarchical Behaviour Spaces

Michael Matthews, Pierluca D'Oro, Anssi Kanervisto, Scott Fujimoto, Jakob Nicolaus Foerster, Mikael Henaff; 7, 2026.
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Planning for Signaling in Low-Trust Environments

Septia Rani, Turgay Caglar, Sarath Sreedharan; 7, 2026.
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Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Program

Jose Efraim Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu, Huazheng Wang; 7, 2026.
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FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies

Chenxiao Gao, Edward Chen, Tianyi Chen, Bo Dai; 7, 2026.
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Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics

Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, Subramanian Ramamoorthy; 7, 2026.
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A Value-Based Approach to Maximum Entropy Exploration

Jacob Adamczyk, Adam Kamoski, Rahul V Kulkarni; 7, 2026.
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Optimal Regret for Policy Optimization in Average Reward MDPs Without Mixing

William Powell, Jeongyeol Kwon, Qiaomin Xie, Hanbaek Lyu; 7, 2026.
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A Simple Baseline for Learning Approximate State Abstractions in Factored State Spaces

Anshuman Senapati, Josiah P. Hanna; 7, 2026.
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Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation

Jake Gonzales, Max Horwitz, Eric Mazumdar, Lillian J. Ratliff; 7, 2026.
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From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning

Rafael Rodriguez-Sanchez, Cameron Allen, George Konidaris; 7, 2026.
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Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration

Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici, Ness Shroff; 7, 2026.
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Decentralized Asymmetric DQN: Decentralization without Factorization in Multi-Agent Reinforcement Learning

Rupali Bhati, Anurag Kadkol, Andrea Baisero, Christopher Amato; 7, 2026.
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When Can Pure Exploitation Succeed in Linear RL? Decoys and Self-Identifiability for Greedy LSVI

Manoj Saravanan, Rohit Kumar Salla; 7, 2026.
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Inference-Time Policy Alignment for Fair Reinforcement Learning

Umer Siddique, Peilang Li, Conor Wallace, Yongcan Cao; 7, 2026.
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Offline RL with Hierarchical Action Chunking

Ahad Jawaid; 7, 2026.
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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning

Andrea Baisero, Rupali Bhati, Shuo Liu, Aathira Sunil Pillai, Christopher Amato; 7, 2026.
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Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning

Parham Mohammad Panahi, Armin Ashrafi, Haoyu Du, Andrew Patterson, Martha White, Adam White; 7, 2026.
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Improving Human Performance with Value-Aware Interventions: A Case Study in Chess

Saumik Narayanan, Raja Panjwani, Siddhartha Sen, Chien-Ju Ho; 7, 2026.
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Learning World Value Functions with Successor Representation and Vision Models

Sergio Frasco, Devon Jarvis, Geraud Nangue Tasse; 7, 2026.
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Best-of-Both-Worlds Multi-Dueling Bandits: Unified Algorithms for Stochastic and Adversarial Preferences under Condorcet and Borda Objectives

S Akash, Pratik Gajane, Jawar Singh; 7, 2026.
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The Open Ant: A Robot Platform for Reinforcement Learning Research

Elena Sorina Lupu, Patrick Spieler, Khurram Javed, Kris De Asis, John D Martin, Martha Steenstrup, Joseph Varughese Modayil; 7, 2026.
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Offline-to-Online Learning in Linear Bandits

Kushagra Chandak, Toshinori Kitamura, Xiaoqi Tan; 7, 2026.
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Annealed Softmax Greedy in Many-Armed Bayesian Bandits

William Overman, Mohsen Bayati; 7, 2026.
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Collaborative Learning under Strategic Behavior: Mechanisms for Eliciting Feedback in Principal-Agent Bandit Games

Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshmi Subramanian, Maximilian Nickel; 7, 2026.
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Learning Communication Skills in Multi-task Multi-agent Deep Reinforcement Learning

Changxi Zhu, Mehdi Dastani, Shihan Wang; 7, 2026.
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Revisiting Adam for Streaming Reinforcement Learning

Florin Gogianu, Luțu Adrian-Cătălin, Razvan Pascanu; 7, 2026.
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Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning

Ziyi Liu, Grace Zhang; 7, 2026.
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Improving Reward-Based Hindsight Credit Assignment

Aditya A. Ramesh, Jiamin He, Jürgen Schmidhuber, Martha White; 7, 2026.
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Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts

Udvas Das, Waris Radji, Debabrota Basu, Odalric-Ambrym Maillard; 7, 2026.
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Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching

Andrea Fraschini, Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli; 7, 2026.
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Q-Based Variational Inverse Reinforcement Learning

Ondrej Bajgar, Peter Tisnikar, Konstantinos Gatsis, Alessandro Abate, Michael A Osborne; 7, 2026.
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ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning

Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner, Pascal Poupart, Jongmin Lee, Kee-Eung Kim; 7, 2026.
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Cohering Reinforcement Learning

Anna Harutyunyan, Will Dabney, Doina Precup; 7, 2026.
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Generalization in Monitored Markov Decision Processes (Mon-MDPs)

Montaser Mohammedalamen, Michael Bowling; 7, 2026.
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Dynamic Object Masks as Goal Representations for Visual Goal-Conditioned Reinforcement Learning

Fahim Shahriar, Cheryl Wang, Seyed Alireza Azimi, Gautham Vasan, Hany Hamed, Abhishek Naik, A. Rupam Mahmood, Colin Bellinger; 7, 2026.
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Gated Q-learning: Add Off-Policy Bias to Taste

Brett Daley; 7, 2026.
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The challenge of hidden gifts in multi-agent reinforcement learning

Dane Malenfant, Blake Aaron Richards; 7, 2026.
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Leveraging Reward Machines for Efficient Multi-Objective Reinforcement Learning

Panos Aronis, Mehdi Dastani, Roxana Rădulescu, Giovanni Varricchione; 7, 2026.
[abs][pdf][bib]

A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation

Xiaocan Li, Shiliang Wu, Zheng Shen; 7, 2026.
[abs][pdf][bib]

Skill-based Safe Reinforcement Learning with Risk Planning

Hanping Zhang, Yuhong Guo; 7, 2026.
[abs][pdf][bib]

Design Principles for Tabular Multi-Policy MORL in Infinite Horizons

Marcelo d'Almeida, Daniel Mosse; 7, 2026.
[abs][pdf][bib]

Discovering High Quality Chess Puzzles with Offline Reinforcement Learning

Allen Nie, Anirudhan Badrinath, Nicholas Tomlin, Timothy Dai, Carissa Yip, Rose E Wang, Emma Brunskill, Christopher J Piech; 7, 2026.
[abs][pdf][bib]

Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach

Alessandro Sestini, Joakim Bergdahl, Jean-Philippe Barrette-LaPierre, Florian Fuchs, Brady Chen, Fabio Zinno, Michael D Jones, Linus Gisslén; 7, 2026.
[abs][pdf][bib]

Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization

Matteo Pannacci, Andrea Fanti, Elena Umili, Roberto Capobianco; 7, 2026.
[abs][pdf][bib]

Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL

Mingxuan Che, Tsung Yuan Tseng, Theresa Eimer, Marius Lindauer, Alexander von Rohr; 7, 2026.
[abs][pdf][bib]