RLJ Volume 6 (2025)

Published as part of RLJ 2025: Volume 6, DOI: 10.5281/zenodo.21537949.

Reinforcement Learning for Finite Space Mean-Field Type Game

Kai Shao, Jiacheng Shen, Mathieu Lauriere; 6:1−39, 2025.
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Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

Ziyan Luo, Tianwei Ni, Pierre-Luc Bacon, Doina Precup, Xujie Si; 6:40−96, 2025.
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Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences

Takuya Hiraoka, Takashi Onishi, Guanquan Wang, Yoshimasa Tsuruoka; 6:97−138, 2025.
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Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback

Qinqing Zheng, Mikael Henaff, Amy Zhang, Aditya Grover, Brandon Amos; 6:139−164, 2025.
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A Finite-Time Analysis of Distributed Q-Learning

Han-Dong Lim, Donghwan Lee; 6:165−200, 2025.
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Finite-Time Analysis of Minimax Q-Learning

Narim Jeong, Donghwan Lee; 6:201−230, 2025.
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Collaboration Promotes Group Resilience in Multi-Agent RL

Ilai Shraga, Guy Azran, Matthias Gerstgrasser, Ofir Abu, Jeffrey Rosenschein, Sarah Keren; 6:231−243, 2025.
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Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks

Joery A. de Vries, Jinke He, Mathijs de Weerdt, Matthijs T. J. Spaan; 6:244−275, 2025.
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Foundation Model Self-Play: Open-Ended Strategy Innovation via Foundation Models

Aaron Dharna, Cong Lu, Jeff Clune; 6:276−342, 2025.
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Action Mapping for Reinforcement Learning in Continuous Environments with Constraints

Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo, Alberto Sangiovanni-Vincentelli; 6:343−363, 2025.
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Chargax: A JAX Accelerated EV Charging Simulator

Koen Ponse, Jan Felix Kleuker, Thomas M. Moerland, Aske Plaat; 6:364−383, 2025.
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Effect of a slowdown correlated to the current state of the environment on an asynchronous learning architecture

Idriss Abdallah, Laurent CIARLETTA, Patrick HENAFF, Jonathan Champagne, Matthieu BONAVENT; 6:384−398, 2025.
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Cascade - A sequential ensemble method for continuous control tasks

Robin Schmöcker, Alexander Dockhorn; 6:399−411, 2025.
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Average-Reward Soft Actor-Critic

Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V Kulkarni; 6:412−430, 2025.
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Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes

Juan Sebastian Rojas, Chi-Guhn Lee; 6:431−477, 2025.
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Your Learned Constraint is Secretly a Backward Reachable Tube

Mohamad Qadri, Gokul Swamy, Jonathan Francis, Michael Kaess, Andrea Bajcsy; 6:478−492, 2025.
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Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism

Kihyun Yu, Duksang Lee, William Overman, Dabeen Lee; 6:493−546, 2025.
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Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies

Jiaqi Chen, Ji Shi, Cansu Sancaktar, Jonas Frey, Georg Martius; 6:547−584, 2025.
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Uncertainty Prioritized Experience Replay

Rodrigo Antonio Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney; 6:585−623, 2025.
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RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$

Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein; 6:624−649, 2025.
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Pareto Optimal Learning from Preferences with Hidden Context

Ryan Bahlous-Boldi, Li Ding, Lee Spector, Scott Niekum; 6:650−670, 2025.
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WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies

William Solow, Sandhya Saisubramanian, Alan Fern; 6:671−689, 2025.
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When and Why Hyperbolic Discounting Matters for Reinforcement Learning Interventions

Ian M. Moore, Eura Nofshin, Siddharth Swaroop, Susan Murphy, Finale Doshi-Velez, Weiwei Pan; 6:690−712, 2025.
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Reinforcement Learning from Human Feedback with High-Confidence Safety Guarantees

Yaswanth Chittepu, Blossom Metevier, Will Schwarzer, Austin Hoag, Scott Niekum, Philip S. Thomas; 6:713−736, 2025.
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AVID: Adapting Video Diffusion Models to World Models

Marc Rigter, Tarun Gupta, Agrin Hilmkil, Chao Ma; 6:737−764, 2025.
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Non-Stationary Latent Auto-Regressive Bandits

Anna L. Trella, Walter H. Dempsey, Asim Gazi, Ziping Xu, Finale Doshi-Velez, Susan Murphy; 6:765−789, 2025.
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Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense

Aditya Vikram Singh, Ethan Rathbun, Emma Graham, Lisa Oakley, Simona Boboila, Peter Chin, Alina Oprea; 6:790−810, 2025.
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The Confusing Instance Principle for Online Linear Quadratic Control

Waris Radji, Odalric-Ambrym Maillard; 6:811−828, 2025.
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Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing

Benedict Hildisch, Edoardo Ghignone, Nicolas Baumann, Cheng Hu, Andrea Carron, Michele Magno; 6:829−861, 2025.
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Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models

Kefan Song, Jin Yao, Runnan Jiang, Rohan Chandra, Shangtong Zhang; 6:862−873, 2025.
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Pure Exploration for Constrained Best Mixed Arm Identification with a Fixed Budget

Dengwang Tang, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo; 6:874−893, 2025.
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Quantitative Resilience Modeling for Autonomous Cyber Defense

Xavier Cadet, Simona Boboila, Edward Koh, Peter Chin, Alina Oprea; 6:894−908, 2025.
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Efficient Information Sharing for Training Decentralized Multi-Agent World Models

Xiaoling Zeng, Qi Zhang; 6:909−922, 2025.
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Recursive Reward Aggregation

Yuting Tang, Yivan Zhang, Johannes Ackermann, Yu-Jie Zhang, Soichiro Nishimori, Masashi Sugiyama; 6:923−975, 2025.
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A Finite-Sample Analysis of an Actor-Critic Algorithm for Mean-Variance Optimization in a Discounted MDP

Tejaram Sangadi, Prashanth L. A., Krishna Jagannathan; 6:976−1024, 2025.
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Impoola: The Power of Average Pooling for Image-based Deep Reinforcement Learning

Raphael Trumpp, Ansgar Schäfftlein, Mirco Theile, Marco Caccamo; 6:1025−1047, 2025.
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Fast Adaptation with Behavioral Foundation Models

Harshit Sikchi, Andrea Tirinzoni, Ahmed Touati, Yingchen Xu, Anssi Kanervisto, Scott Niekum, Amy Zhang, Alessandro Lazaric, Matteo Pirotta; 6:1048−1074, 2025.
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Multi-Task Reinforcement Learning Enables Parameter Scaling

Reginald McLean, Evangelos Chatzaroulas, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro; 6:1075−1093, 2025.
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Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning

Théo Vincent, Tim Faust, Yogesh Tripathi, Jan Peters, Carlo D'Eramo; 6:1094−1119, 2025.
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Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning

Alihan Hüyük, Arndt Ryo Koblitz, Atefeh Mohajeri Moghaddam, Matthew Andrews; 6:1120−1139, 2025.
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Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization

Sebastian Griesbach, Carlo D'Eramo; 6:1140−1157, 2025.
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Nonparametric Policy Improvement in Continuous Action Spaces via Expert Demonstrations

Agustin Castellano, Sohrab Rezaei, Jared Markowitz, Enrique Mallada; 6:1158−1179, 2025.
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DisDP: Robust Imitation Learning via Disentangled Diffusion Policies

Pankhuri Vanjani, Paul Mattes, Xiaogang Jia, Vedant Dave, Rudolf Lioutikov; 6:1180−1199, 2025.
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Mitigating Goal Misgeneralization via Minimax Regret

Karim Abdel Sadek, Matthew Farrugia-Roberts, Usman Anwar, Hannah Erlebach, Christian Schroeder de Witt, David Krueger, Michael D Dennis; 6:1200−1246, 2025.
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Long-Horizon Planning with Predictable Skills

Nico Gürtler, Georg Martius; 6:1247−1272, 2025.
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HANQ: Hypergradients, Asymmetry, and Normalization for Fast and Stable Deep $Q$-Learning

Braham Snyder, Chen-Yu Wei; 6:1273−1292, 2025.
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Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks

Viraj Joshi, Zifan Xu, Bo Liu, Peter Stone, Amy Zhang; 6:1293−1317, 2025.
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Optimal discounting for offline input-driven MDP

Randy Lefebvre, Audrey Durand; 6:1318−1333, 2025.
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Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions

Kyungmin Kim, JB Lanier, Roy Fox; 6:1334−1364, 2025.
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Reinforcement Learning for Human-AI Collaboration via Probabilistic Intent Inference

Yuxin Lin, Seyede Fatemeh Ghoreishi, Tian Lan, Mahdi Imani; 6:1365−1377, 2025.
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PufferLib 2.0: Reinforcement Learning at 1M steps/s

Joseph Suarez; 6:1378−1388, 2025.
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Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL

Ömer Veysel Çağatan, Baris Akgun; 6:1389−1411, 2025.
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Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains

Ruo Yu Tao, Kaicheng Guo, Cameron Allen, George Konidaris; 6:1412−1439, 2025.
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Rectifying Regression in Reinforcement Learning

Alex Ayoub, David Szepesvari, Alireza Bakhtiari, Csaba Szepesvari, Dale Schuurmans; 6:1440−1454, 2025.
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High-Confidence Policy Improvement from Human Feedback

Hon Tik Tse, Philip S. Thomas, Scott Niekum; 6:1455−1479, 2025.
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Adaptive Reward Sharing to Enhance Learning in the Context of Multiagent Teams

Kyle Tilbury, David Radke; 6:1480−1495, 2025.
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MixUCB: Enhancing Safe Exploration in Contextual Bandits with Human Oversight

Jinyan Su, Rohan Banerjee, Jiankai Sun, Wen Sun, Sarah Dean; 6:1496−1520, 2025.
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Efficient Morphology-Aware Policy Transfer to New Embodiments

Michael Przystupa, Hongyao Tang, Glen Berseth, Mariano Phielipp, Santiago Miret, Martin Jägersand, Matthew E. Taylor; 6:1521−1539, 2025.
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Understanding Learned Representations and Action Collapse in Visual Reinforcement Learning

Xi Chen, Zhihui Zhu, Andrew Perrault; 6:1540−1557, 2025.
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Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions

Ayush Jain, Norio Kosaka, Xinhu Li, Kyung-Min Kim, Erdem Biyik, Joseph J Lim; 6:1558−1599, 2025.
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Leveraging priors on distribution functions for multi-arm bandits

Sumit Vashishtha, Odalric-Ambrym Maillard; 6:1600−1623, 2025.
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ProtoCRL: Prototype-based Network for Continual Reinforcement Learning

Michela Proietti, Peter R. Wurman, Peter Stone, Roberto Capobianco; 6:1624−1646, 2025.
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Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting

Edoardo Cetin, Ahmed Touati, Yann Ollivier; 6:1647−1680, 2025.
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Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning

Subhojyoti Mukherjee, Josiah P. Hanna, Qiaomin Xie, Robert D Nowak; 6:1681−1723, 2025.
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Multi-task Representation Learning for Fixed Budget Pure-Exploration in Linear and Bilinear Bandits

Subhojyoti Mukherjee, Qiaomin Xie, Robert D Nowak; 6:1724−1772, 2025.
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Offline Reinforcement Learning with Domain-Unlabeled Data

Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama; 6:1773−1793, 2025.
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Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

Yannik Mahlau, Maximilian Schier, Christoph Reinders, Frederik Schubert, Marco Bügling, Bodo Rosenhahn; 6:1794−1815, 2025.
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Syllabus: Portable Curricula for Reinforcement Learning Agents

Ryan Sullivan, Ryan Pégoud, Ameen Ur Rehman, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P Dickerson; 6:1816−1855, 2025.
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Exploration-Free Reinforcement Learning with Linear Function Approximation

Luca Civitavecchia, Matteo Papini; 6:1856−1879, 2025.
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SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning

Carlo Romeo, Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov; 6:1880−1893, 2025.
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Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning

Abdul Wahab, Raksha Kumaraswamy, Martha White; 6:1894−1915, 2025.
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Gaussian Process Q-Learning for Finite-Horizon Markov Decision Processes

Maximilian Bloor, Tom Savage, Calvin Tsay, Antonio Del rio chanona, Max Mowbray; 6:1916−1930, 2025.
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On the Effect of Regularization in Policy Mirror Descent

Jan Felix Kleuker, Aske Plaat, Thomas M. Moerland; 6:1931−1950, 2025.
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Concept-Based Off-Policy Evaluation

Ritam Majumdar, Jack Teversham, Sonali Parbhoo; 6:1951−1989, 2025.
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Investigating the Utility of Mirror Descent in Off-policy Actor-Critic

Samuel Neumann, Jiamin He, Adam White, Martha White; 6:1990−2022, 2025.
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Hybrid Classical/RL Local Planner for Ground Robot Navigation

Vishnu Dutt Sharma, Jeongran Lee, Matthew Andrews, Ilija Hadžić; 6:2023−2037, 2025.
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How Should We Meta-Learn Reinforcement Learning Algorithms?

Alexander David Goldie, Zilin Wang, Jaron Cohen, Jakob Nicolaus Foerster, Shimon Whiteson; 6:2038−2081, 2025.
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Seldonian Reinforcement Learning for Ad Hoc Teamwork

Edoardo Zorzi, Alberto Castellini, Leonidas Bakopoulos, Georgios Chalkiadakis, Alessandro Farinelli; 6:2082−2100, 2025.
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Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps

Motoki Omura, Yusuke Mukuta, Kazuki Ota, Takayuki Osa, Tatsuya Harada; 6:2101−2114, 2025.
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Intrinsically Motivated Discovery of Temporally Abstract Graph-based Models of the World

Akhil Bagaria, Anita De Mello Koch, Rafael Rodriguez-Sanchez, Sam Lobel, George Konidaris; 6:2115−2134, 2025.
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An Optimisation Framework for Unsupervised Environment Design

Nathan Monette, Alistair Letcher, Michael Beukman, Matthew Thomas Jackson, Alexander Rutherford, Alexander David Goldie, Jakob Nicolaus Foerster; 6:2135−2158, 2025.
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Epistemically-guided forward-backward exploration

Núria Armengol Urpí, Marin Vlastelica, Georg Martius, Stelian Coros; 6:2159−2173, 2025.
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Rethinking the Foundations for Continual Reinforcement Learning

Esraa Elelimy, David Szepesvari, Martha White, Michael Bowling; 6:2174−2194, 2025.
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Modelling human exploration with light-weight meta reinforcement learning algorithms

Thomas D. Ferguson, Alona Fyshe, Adam White; 6:2195−2209, 2025.
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Zero-Shot Reinforcement Learning Under Partial Observability

Scott Jeen, Tom Bewley, Jonathan Cullen; 6:2210−2233, 2025.
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Building Sequential Resource Allocation Mechanisms without Payments

Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh; 6:2234−2255, 2025.
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From Explainability to Interpretability: Interpretable Reinforcement Learning Via Model Explanations

Peilang Li, Umer Siddique, Yongcan Cao; 6:2256−2270, 2025.
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Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control

Justin Turnau, Longchao Da, Khoa Vo, Ferdous Al Rafi, Shreyas Bachiraju, Tiejin Chen, Hua Wei; 6:2271−2290, 2025.
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Sampling from Energy-based Policies using Diffusion

Vineet Jain, Tara Akhound-Sadegh, Siamak Ravanbakhsh; 6:2291−2307, 2025.
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Multiple-Frequencies Population-Based Training

Waël Doulazmi, Auguste Lehuger, Marin Toromanoff, Valentin Charraut, Thibault Buhet, Fabien Moutarde; 6:2308−2326, 2025.
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TransAM: Transformer-Based Agent Modeling for Multi-Agent Systems via Local Trajectory Encoding

Conor Wallace, Umer Siddique, Yongcan Cao; 6:2327−2341, 2025.
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Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners

Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli, Serena Booth, W. Bradley Knox, Matthew E. Taylor; 6:2342−2367, 2025.
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Optimistic critics can empower small actors

Olya Mastikhina, Dhruv Sreenivas, Pablo Samuel Castro; 6:2368−2387, 2025.
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PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning

Ondrej Bajgar, Dewi Sid William Gould, Jonathon Liu, Alessandro Abate, Konstantinos Gatsis, Michael A Osborne; 6:2388−2414, 2025.
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AVG-DICE: Stationary Distribution Correction by Regression

Fengdi Che, Bryan Chan, Chen Ma, A. Rupam Mahmood; 6:2415−2426, 2025.
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V-Max: A RL Framework for Autonomous Driving

Valentin Charraut, Waël Doulazmi, Thomas Tournaire, Thibault Buhet; 6:2427−2451, 2025.
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Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets

Alexander Levine, Peter Stone, Amy Zhang; 6:2452−2484, 2025.
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One Goal, Many Challenges: Robust Preference Optimization Amid Content-Aware, Multi-Source Noise

Amirabbas Afzali, Amirhossein Afsharrad, Seyed Shahabeddin Mousavi, Sanjay Lall; 6:2485−2507, 2025.
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A Timer-Based Hybrid Supervisor for Robust, Chatter-Free Policy Switching

Jan de Priester, Ricardo Sanfelice; 6:2508−2529, 2025.
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Deep Reinforcement Learning with Gradient Eligibility Traces

Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Martha White; 6:2530−2550, 2025.
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On Slowly-varying Non-stationary Bandits

Ramakrishnan K, Aditya Gopalan; 6:2551−2584, 2025.
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Focused Skill Discovery: Learning to Control Specific State Variables while Minimizing Side Effects

Jonathan Colaço Carr, Qinyi Sun, Cameron Allen; 6:2585−2599, 2025.
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Goals vs. Rewards: A Preliminary Comparative Study of Objective Specification Mechanisms

Septia Rani, Serena Booth, Sarath Sreedharan; 6:2600−2618, 2025.
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An Analysis of Action-Value Temporal-Difference Methods That Learn State Values

Brett Daley, Prabhat Nagarajan, Martha White, Marlos C. Machado; 6:2619−2636, 2025.
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PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL

Joshua McClellan, Greyson Brothers, Furong Huang, Pratap Tokekar; 6:2637−2651, 2025.
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Shaping Laser Pulses with Reinforcement Learning

Francesco Capuano, Davorin Peceli, Gabriele Tiboni; 6:2652−2666, 2025.
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Reinforcement Learning with Adaptive Temporal Discounting

Sahaj Singh Maini, Zoran Tiganj; 6:2667−2684, 2025.
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Human-Level Competitive Pokémon via Scalable Offline Reinforcement Learning with Transformers

Jake Grigsby, Yuqi Xie, Justin Sasek, Steven Zheng, Yuke Zhu; 6:2685−2719, 2025.
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Adaptive Submodular Policy Optimization

Branislav Kveton, Anup Rao, Viet Dac Lai, Nikos Vlassis, David Arbour; 6:2720−2736, 2025.
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Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

Umer Siddique, Peilang Li, Yongcan Cao; 6:2737−2761, 2025.
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Representation Learning and Skill Discovery with Empowerment

Andrew Levy, Alessandro G Allievi, George Konidaris; 6:2762−2787, 2025.
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Empirical Bound Information-Directed Sampling for Norm-Agnostic Bandits

Piotr M. Suder, Eric Laber; 6:2788−2819, 2025.
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Thompson Sampling for Constrained Bandits

Rohan Deb, Mohammad Ghavamzadeh, Arindam Banerjee; 6:2820−2843, 2025.
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AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability

Fernando Rosas, Alexander Boyd, Manuel Baltieri; 6:2844−2881, 2025.
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Achieving Limited Adaptivity for Multinomial Logistic Bandits

Sukruta Prakash Midigeshi, Tanmay Goyal, Gaurav Sinha; 6:2882−2896, 2025.
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