Accepted Papers — RLC 2025

Action Mapping for Reinforcement Learning in Continuous Environments with Constraints

Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo, Alberto Sangiovanni-Vincentelli

Chargax: A JAX Accelerated EV Charging Simulator

Koen Ponse, Jan Felix Kleuker, Thomas M. Moerland, Aske Plaat

WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies

William Solow, Sandhya Saisubramanian, Alan Fern

Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing

Benedict Hildisch, Edoardo Ghignone, Nicolas Baumann, Cheng Hu, Andrea Carron, Michele Magno

Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

Yannik Mahlau, Maximilian Schier, Christoph Reinders, Frederik Schubert, Marco Bügling, Bodo Rosenhahn

Gaussian Process Q-Learning for Finite-Horizon Markov Decision Process

Maximilian Bloor, Tom Savage, Calvin Tsay, Antonio Del rio chanona, Max Mowbray

Hybrid Classical/RL Local Planner for Ground Robot Navigation

Vishnu Dutt Sharma, Jeongran Lee, Matthew Andrews, Ilija Hadžić

V-Max: Making RL Practical for Autonomous Driving

Valentin Charraut, Waël Doulazmi, Thomas Tournaire, Thibault Buhet

Shaping Laser Pulses with Reinforcement Learning

Francesco Capuano, Davorin Peceli, Gabriele Tiboni

Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics

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

Average-Reward Soft Actor-Critic

Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V Kulkarni

Your Learned Constraint is Secretly a Backward Reachable Tube

Mohamad Qadri, Gokul Swamy, Jonathan Francis, Michael Kaess, Andrea Bajcsy

Recursive Reward Aggregation

Yuting Tang, Yivan Zhang, Johannes Ackermann, Yu-Jie Zhang, Soichiro Nishimori, Masashi Sugiyama

Investigating the Utility of Mirror Descent in Off-policy Actor-Critic

Samuel Neumann, Jiamin He, Adam White, Martha White

Rethinking the Foundations for Continual Reinforcement Learning

Esraa Elelimy, David Szepesvari, Martha White, Michael Bowling

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values

Brett Daley, Prabhat Nagarajan, Martha White, Marlos C. Machado

Reinforcement Learning with Adaptive Temporal Discounting

Sahaj Singh Maini, Zoran Tiganj

Reinforcement Learning for Finite Space Mean-Field Type Game

Kai Shao, Jiacheng Shen, Mathieu Lauriere

Collaboration Promotes Group Resilience in Multi-Agent RL

Ilai Shraga, Guy Azran, Matthias Gerstgrasser, Ofir Abu, Jeffrey Rosenschein, Sarah Keren

Foundation Model Self-Play: Open-Ended Strategy Innovation via Foundation Models

Aaron Dharna, Cong Lu, Jeff Clune

Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense

Aditya Vikram Singh, Ethan Rathbun, Emma Graham, Lisa Oakley, Simona Boboila, Peter Chin, Alina Oprea

Efficient Information Sharing for Training Decentralized Multi-Agent World Models

Xiaoling Zeng, Qi Zhang

Adaptive Reward Sharing to Enhance Learning in the Context of Multiagent Teams

Kyle Tilbury, David Radke

Seldonian Reinforcement Learning for Ad Hoc Teamwork

Edoardo Zorzi, Alberto Castellini, Leonidas Bakopoulos, Georgios Chalkiadakis, Alessandro Farinelli

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

TransAM: Transformer-Based Agent Modeling for Multi-Agent Systems via Local Trajectory Encoding

Conor Wallace, Umer Siddique, Yongcan Cao

PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL

Joshua McClellan, Greyson Brothers, Furong Huang, Pratap Tokekar

Human-Level Competitive Pokémon via Scalable Offline Reinforcement Learning with Transformers

Jake Grigsby, Yuqi Xie, Justin Sasek, Steven Zheng, Yuke Zhu

Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks

Joery A. de Vries, Jinke He, Mathijs de Weerdt, Matthijs T. J. Spaan

Cascade - A sequential ensemble method for continuous control tasks

Robin Schmöcker, Alexander Dockhorn

HANQ: Hypergradients, Asymmetry, and Normalization for Fast and Stable Deep Q-Learning

Braham Snyder, Chen-Yu Wei

Rectifying Regression in Reinforcement Learning

Alex Ayoub, David Szepesvari, Alireza Bakhtiari, Csaba Szepesvari, Dale Schuurmans

Efficient Morphology-Aware Policy Transfer to New Embodiments

Michael Przystupa, Hongyao Tang, Glen Berseth, Mariano Phielipp, Santiago Miret, Martin Jägersand, Matthew E. Taylor

Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting

Edoardo Cetin, Ahmed Touati, Yann Ollivier

Concept-Based Off-Policy Evaluation

Ritam Majumdar, Jack Teversham, Sonali Parbhoo

Multiple-Frequencies Population-Based Training

Waël Doulazmi, Auguste Lehuger, Marin Toromanoff, Valentin Charraut, Thibault Buhet, Fabien Moutarde

AVG-DICE: Stationary Distribution Correction by Regression

Fengdi Che, Bryan Chan, Chen Ma, A. Rupam Mahmood

Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning

Understanding the Effectiveness of Learning Behavioral Metrics in Deep Reinforcement Learning

Impoola: The Power of Average Pooling for Image-based Deep Reinforcement Learning

Raphael Trumpp, Ansgar Schäfftlein, Mirco Theile, Marco Caccamo

Eau De Q-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning

Théo Vincent, Tim Faust, Yogesh Tripathi, Jan Peters, Carlo D'Eramo

Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning

Alihan Hüyük, Arndt Ryo Koblitz, Atefeh Mohajeri Moghaddam, Matthew Andrews

Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions

Kyungmin Kim, JB Lanier, Roy Fox

Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning

Subhojyoti Mukherjee, Josiah P. Hanna, Qiaomin Xie, Robert D Nowak

Sampling from Energy-based Policies using Diffusion

Vineet Jain, Tara Akhound-Sadegh, Siamak Ravanbakhsh

Optimistic critics can empower small actors

Olya Mastikhina, Dhruv Sreenivas, Pablo Samuel Castro

Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning

Deep Reinforcement Learning with Gradient Eligibility Traces

Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Martha White

Uncertainty Prioritized Experience Replay

Rodrigo Antonio Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney

Pure Exploration for Constrained Best Mixed Arm Identification with a Fixed Budget

Dengwang Tang, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo

Quantitative Resilience Modeling for Autonomous Cyber Defense

Xavier Cadet, Simona Boboila, Edward Koh, Peter Chin, Alina Oprea

Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization

Sebastian Griesbach, Carlo D'Eramo

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

Exploration-Free Reinforcement Learning with Linear Function Approximation

Luca Civitavecchia, Matteo Papini

Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning

Abdul Wahab, Raksha Kumaraswamy, Martha White

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

An Optimisation Framework for Unsupervised Environment Design

Nathan Monette, Alistair Letcher, Michael Beukman, Matthew Thomas Jackson, Alexander Rutherford, Alexander David Goldie, Jakob Nicolaus Foerster

Epistemically-guided forward-backward exploration

Núria Armengol Urpí, Marin Vlastelica, Georg Martius, Stelian Coros

RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning

Pareto Optimal Learning from Preferences with Hidden Context

Ryan Bahlous-Boldi, Li Ding, Lee Spector, Scott Niekum

When and Why Hyperbolic Discounting Matters for Reinforcement Learning Interventions

Ian M. Moore, Eura Nofshin, Siddharth Swaroop, Susan Murphy, Finale Doshi-Velez, Weiwei Pan

Reinforcement Learning from Human Feedback with High-Confidence Safety Guarantees

Yaswanth Chittepu, Blossom Metevier, Will Schwarzer, Austin Hoag, Scott Niekum, Philip S. Thomas

Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models

Kefan Song, Jin Yao, Runnan Jiang, Rohan Chandra, Shangtong Zhang

Reinforcement Learning for Human-AI Collaboration via Probabilistic Intent Inference

Yuxin Lin, Seyede Fatemeh Ghoreishi, Tian Lan, Mahdi Imani

High-Confidence Policy Improvement from Human Feedback

Hon Tik Tse, Philip S. Thomas, Scott Niekum

Building Sequential Resource Allocation Mechanisms without Payments

Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh

From Explainability to Interpretability: Interpretable Reinforcement Learning Via Model Explanations

Peilang Li, Umer Siddique, Yongcan Cao

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

Umer Siddique, Peilang Li, Yongcan Cao

AI in a vat: Fundamental limits of efficient world modelling for safe agent sandboxing

Fernando Rosas, Alexander Boyd, Manuel Baltieri

A Finite-Time Analysis of Distributed Q-Learning

Han-Dong Lim, Donghwan Lee

Finite-Time Analysis of Minimax Q-Learning

Narim Jeong, Donghwan Lee

Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism

Kihyun Yu, Duksang Lee, William Overman, Dabeen Lee

Non-Stationary Latent Auto-Regressive Bandits

Anna L. Trella, Walter H. Dempsey, Asim Gazi, Ziping Xu, Finale Doshi-Velez, Susan Murphy

A Finite-Sample Analysis of an Actor-Critic Algorithm for Mean-Variance Optimization in a Discounted MDP

Tejaram Sangadi, Prashanth L. A., Krishna Jagannathan

Leveraging priors on distribution functions for multi-arm bandits

Sumit Vashishtha, Odalric-Ambrym Maillard

Multi-task Representation Learning for Fixed Budget Pure-Exploration in Linear and Bilinear Bandits

Subhojyoti Mukherjee, Qiaomin Xie, Robert D Nowak

On Slowly-varying Non-stationary Bandits

Ramakrishnan K, Aditya Gopalan

Empirical Bound Information-Directed Sampling

Piotr M. Suder, Eric Laber

Thompson Sampling for Constrained Bandits

Rohan Deb, Mohammad Ghavamzadeh, Arindam Banerjee

Achieving Limited Adaptivity for Multinomial Logistic Bandits

Sukruta Prakash Midigeshi, Tanmay Goyal, Gaurav Sinha

Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences

Takuya Hiraoka, Takashi Onishi, Guanquan Wang, Yoshimasa Tsuruoka

Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies

Jiaqi Chen, Ji Shi, Cansu Sancaktar, Jonas Frey, Georg Martius

Multi-Task Reinforcement Learning Enables Parameter Scaling

Reginald McLean, Evangelos Chatzaroulas, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks

Viraj Joshi, Zifan Xu, Bo Liu, Peter Stone, Amy Zhang

PufferLib 2.0: Reinforcement Learning at 1M steps/s

Joseph Suarez

Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL

Ömer Veysel Çağatan, Baris Akgun

Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains

Ruo Yu Tao, Kaicheng Guo, Cameron Allen, George Konidaris

How Should We Meta-Learn Reinforcement Learning Algorithms?

Alexander David Goldie, Zilin Wang, Jaron Cohen, Jakob Nicolaus Foerster, Shimon Whiteson

AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents

MixUCB: Enhancing Safe Exploration in Contextual Bandits with Human Oversight

Jinyan Su, Rohan Banerjee, Jiankai Sun, Wen Sun, Sarah Dean

AVID: Adapting Video Diffusion Models to World Models

Marc Rigter, Tarun Gupta, Agrin Hilmkil, Chao Ma

The Confusing Instance Principle for Online Linear Quadratic Control

Waris Radji, Odalric-Ambrym Maillard

Long-Horizon Planning with Predictable Skills

Nico Gürtler, Georg Martius

Optimal discounting for offline input-driven MDP

Randy Lefebvre, Audrey Durand

A Timer-Enforced Hybrid Supervisor for Robust, Chatter-Free Policy Switching

Jan de Priester, Ricardo Sanfelice

Focused Skill Discovery: Learning to Control Specific State Variables while Minimizing Side Effects

Jonathan Colaço Carr, Qinyi Sun, Cameron Allen

Representation Learning and Skill Discovery with Empowerment

Andrew Levy, Alessandro G Allievi, George Konidaris

Compositional Instruction Following with Language Models and Reinforcement Learning

Composition and Zero-Shot Transfer with Lattice Structures in Reinforcement Learning

Double Horizon Model-Based Policy Optimization

Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes

Juan Sebastian Rojas, Chi-Guhn Lee

RL³: Boosting Meta Reinforcement Learning via RL inside RL²

Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein

Fast Adaptation with Behavioral Foundation Models

Harshit Sikchi, Andrea Tirinzoni, Ahmed Touati, Yingchen Xu, Anssi Kanervisto, Scott Niekum, Amy Zhang, Alessandro Lazaric, Matteo Pirotta

Understanding Learned Representations and Action Collapse in Visual Reinforcement Learning

Xi Chen, Zhihui Zhu, Andrew Perrault

Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions

Ayush Jain, Norio Kosaka, Xinhu Li, Kyung-Min Kim, Erdem Biyik, Joseph J Lim

ProtoCRL: Prototype-based Network for Continual Reinforcement Learning

Michela Proietti, Peter R. Wurman, Peter Stone, Roberto Capobianco

Offline Reinforcement Learning with Domain-Unlabeled Data

Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama

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

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps

Motoki Omura, Yusuke Mukuta, Kazuki Ota, Takayuki Osa, Tatsuya Harada

Zero-Shot Reinforcement Learning Under Partial Observability

Scott Jeen, Tom Bewley, Jonathan Cullen

Adaptive Submodular Policy Optimization

Branislav Kveton, Anup Rao, Viet Dac Lai, Nikos Vlassis, David Arbour

Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback

Qinqing Zheng, Mikael Henaff, Amy Zhang, Aditya Grover, Brandon Amos

Nonparametric Policy Improvement in Continuous Action Spaces via Expert Demonstrations

Agustin Castellano, Sohrab Rezaei, Jared Markowitz, Enrique Mallada

DisDP: Robust Imitation Learning via Disentangled Diffusion Policies

Pankhuri Vanjani, Paul Mattes, Xiaogang Jia, Vedant Dave, Rudolf Lioutikov

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

Modelling human exploration with light-weight meta reinforcement learning algorithms

Thomas D. Ferguson, Alona Fyshe, Adam White

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

PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning

Ondrej Bajgar, Dewi Sid William Gould, Jonathon Liu, Alessandro Abate, Konstantinos Gatsis, Michael A Osborne

Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets

Alexander Levine, Peter Stone, Amy Zhang

One Goal, Many Challenges: Robust Preference Optimization Amid Content-Aware and Multi-Source Noise

Amirabbas Afzali, Amirhossein Afsharrad, Seyed Shahabeddin Mousavi, Sanjay Lall

Goals vs. Rewards: A Comparative Study of Objective Specification Mechanisms

Septia Rani, Serena Booth, Sarath Sreedharan