RLJ Volumes 1–5 (2024)

Published as part of RLJ 2024: Volumes 1–5, DOI: 10.5281/zenodo.13899776.

Co-Learning Empirical Games & World Models

Max Olan Smith, Michael P. Wellman; 1:1−15, 2024.
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Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits

Woojin Jeong, Seungki Min; 1:16−28, 2024.
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Graph Neural Thompson Sampling

Shuang Wu, Arash A. Amini; 1:29−63, 2024.
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JoinGym: An Efficient Join Order Selection Environment

Junxiong Wang, Kaiwen Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun; 1:64−91, 2024.
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An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks

Antonin Raffin, Olivier Sigaud, Jens Kober, Alin Albu-Schaeffer, João Silvério, Freek Stulp; 1:92−107, 2024.
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Online Planning in POMDPs with State-Requests

Raphaël Avalos, Eugenio Bargiacchi, Ann Nowe, Diederik Roijers, Frans A Oliehoek; 1:108−129, 2024.
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A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning

Abdulaziz Almuzairee, Nicklas Hansen, Henrik I Christensen; 1:130−157, 2024.
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BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations

Robert J. Moss, Anthony Corso, Jef Caers, Mykel Kochenderfer; 1:158−181, 2024.
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Non-adaptive Online Finetuning for Offline Reinforcement Learning

Audrey Huang, Mohammad Ghavamzadeh, Nan Jiang, Marek Petrik; 1:182−197, 2024.
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Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning

Nicholas E. Corrado, Yuxiao Qu, John U. Balis, Adam Labiosa, Josiah P. Hanna; 1:198−215, 2024.
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Towards Principled, Practical Policy Gradient for Bandits and Tabular MDPs

Michael Lu, Matin Aghaei, Anant Raj, Sharan Vaswani; 1:216−282, 2024.
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Unifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets

Benjamin Freed, Thomas Wei, Roberto Calandra, Jeff Schneider, Howie Choset; 1:283−301, 2024.
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The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation

Noah Golowich, Ankur Moitra; 1:302−341, 2024.
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Learning Action-based Representations Using Invariance

Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang; 1:342−365, 2024.
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Cyclicity-Regularized Coordination Graphs

Oliver Järnefelt, Mahdi Kallel, Carlo D'Eramo; 1:366−379, 2024.
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Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization

Aditya Kapoor, Benjamin Freed, Jeff Schneider, Howie Choset; 1:380−399, 2024.
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OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

Quentin Delfosse, Jannis Blüml, Bjarne Gregori, Sebastian Sztwiertnia, Kristian Kersting; 1:400−449, 2024.
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SplAgger: Split Aggregation for Meta-Reinforcement Learning

Jacob Beck, Matthew Thomas Jackson, Risto Vuorio, Zheng Xiong, Shimon Whiteson; 1:450−469, 2024.
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A Tighter Convergence Proof of Reverse Experience Replay

Nan Jiang, Jinzhao Li, Yexiang Xue; 1:470−480, 2024.
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Learning to Optimize for Reinforcement Learning

Qingfeng Lan, A. Rupam Mahmood, Shuicheng YAN, Zhongwen Xu; 2:481−497, 2024.
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Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras

Mhairi Dunion, Stefano V Albrecht; 2:498−515, 2024.
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Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning

Trevor McInroe, Adam Jelley, Stefano V Albrecht, Amos Storkey; 2:516−546, 2024.
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Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning

Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed, Glen Berseth; 2:547−562, 2024.
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Mitigating the Curse of Horizon in Monte-Carlo Returns

Alex Ayoub, David Szepesvari, Francesco Zanini, Bryan Chan, Dhawal Gupta, Bruno Castro da Silva, Dale Schuurmans; 2:563−572, 2024.
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A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization

Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart; 2:573−592, 2024.
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ROIL: Robust Offline Imitation Learning without Trajectories

Gersi Doko, Guang Yang, Daniel S. Brown, Marek Petrik; 2:593−605, 2024.
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Harnessing Discrete Representations for Continual Reinforcement Learning

Edan Jacob Meyer, Adam White, Marlos C. Machado; 2:606−628, 2024.
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Three Dogmas of Reinforcement Learning

David Abel, Mark K Ho, Anna Harutyunyan; 2:629−644, 2024.
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Policy Gradient with Active Importance Sampling

Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli; 2:645−675, 2024.
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The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough

Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti; 2:676−692, 2024.
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Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning

Zakariae EL ASRI, Olivier Sigaud, Nicolas THOME; 2:693−713, 2024.
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Trust-based Consensus in Multi-Agent Reinforcement Learning Systems

Ho Long Fung, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi; 2:714−732, 2024.
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Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies

Yu Luo, Fuchun Sun, Tianying Ji, Xianyuan Zhan; 2:733−762, 2024.
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Informed POMDP: Leveraging Additional Information in Model-Based RL

Gaspard Lambrechts, Adrien Bolland, Damien Ernst; 2:763−784, 2024.
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An Optimal Tightness Bound for the Simulation Lemma

Sam Lobel, Ronald Parr; 2:785−797, 2024.
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Best Response Shaping

Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron Courville; 2:798−818, 2024.
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A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning

Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli; 2:819−839, 2024.
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SwiftTD: A Fast and Robust Algorithm for Temporal Difference Learning

Khurram Javed, Arsalan Sharifnassab, Richard S. Sutton; 2:840−863, 2024.
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The Cliff of Overcommitment with Policy Gradient Step Sizes

Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas; 2:864−883, 2024.
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Multistep Inverse Is Not All You Need

Alexander Levine, Peter Stone, Amy Zhang; 2:884−925, 2024.
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Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors

Emma Cramer, Bernd Frauenknecht, Ramil Sabirov, Sebastian Trimpe; 2:926−945, 2024.
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Sequential Decision-Making for Inline Text Autocomplete

Rohan Chitnis, Shentao Yang, Alborz Geramifard; 2:946−960, 2024.
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Exploring Uncertainty in Distributional Reinforcement Learning

Georgy Antonov, Peter Dayan; 2:961−978, 2024.
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Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning

Marcel Hussing, Jorge Mendez-Mendez, Anisha Singrodia, Cassandra Kent, Eric Eaton; 2:979−994, 2024.
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Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Marcel Hussing, Claas A Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, Eric Eaton; 2:995−1018, 2024.
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Demystifying the Recency Heuristic in Temporal-Difference Learning

Brett Daley, Marlos C. Machado, Martha White; 3:1019−1036, 2024.
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On the consistency of hyper-parameter selection in value-based deep reinforcement learning

Johan Samir Obando Ceron, João Guilherme Madeira Araújo, Aaron Courville, Pablo Samuel Castro; 3:1037−1059, 2024.
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Value Internalization: Learning and Generalizing from Social Reward

Frieda Rong, Max Kleiman-Weiner; 3:1060−1071, 2024.
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Mixture of Experts in a Mixture of RL settings

Timon Willi, Johan Samir Obando Ceron, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Pablo Samuel Castro; 3:1072−1105, 2024.
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Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning

Davide Corsi, Davide Camponogara, Alessandro Farinelli; 3:1106−1123, 2024.
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On Welfare-Centric Fair Reinforcement Learning

Cyrus Cousins, Kavosh Asadi, Elita Lobo, Michael Littman; 3:1124−1137, 2024.
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Inverse Reinforcement Learning with Multiple Planning Horizons

Jiayu Yao, Weiwei Pan, Finale Doshi-Velez, Barbara E Engelhardt; 3:1138−1167, 2024.
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Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation

Yixuan Zhang, Qiaomin Xie; 3:1168−1210, 2024.
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More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling

Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu; 3:1211−1235, 2024.
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Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis

Qining Zhang, Honghao Wei, Lei Ying; 3:1236−1251, 2024.
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A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage

Kevin Tan, Ziping Xu; 3:1252−1264, 2024.
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Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior

Zhiyuan Zhou, Shreyas Sundara Raman, Henry Sowerby, Michael Littman; 3:1265−1288, 2024.
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Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Bin Hu, Chenyang Zhao, Pu Zhang, Zihao Zhou, Yuanhang Yang, Zenglin Xu, Bin Liu; 3:1289−1305, 2024.
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An Idiosyncrasy of Time-discretization in Reinforcement Learning

Kris De Asis, Richard S. Sutton; 3:1306−1316, 2024.
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Dreaming of Many Worlds: Learning Contextual World Models aids Zero-Shot Generalization

Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp; 3:1317−1350, 2024.
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Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes

Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe, Peinan Zhang; 3:1351−1376, 2024.
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Offline Diversity Maximization under Imitation Constraints

Marin Vlastelica, Jin Cheng, Georg Martius, Pavel Kolev; 3:1377−1409, 2024.
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Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace

Léopold Maytié, Benjamin Devillers, Alexandre Arnold, Rufin VanRullen; 3:1410−1426, 2024.
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Stabilizing Extreme Q-learning by Maclaurin Expansion

Motoki Omura, Takayuki Osa, YUSUKE Mukuta, Tatsuya Harada; 3:1427−1440, 2024.
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Combining Automated Optimisation of Hyperparameters and Reward Shape

Julian Dierkes, Emma Cramer, Holger Hoos, Sebastian Trimpe; 3:1441−1466, 2024.
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Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

He Wang, Laixi Shi, Yuejie Chi; 3:1467−1510, 2024.
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PASTA: Pretrained Action-State Transformer Agents

Raphael Boige, Yannis Flet-Berliac, Lars C.P.M. Quaedvlieg, Arthur Flajolet, Guillaume Richard, Thomas PIERROT; 3:1511−1532, 2024.
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Cost Aware Best Arm Identification

Kellen Kanarios, Qining Zhang, Lei Ying; 4:1533−1545, 2024.
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ICU-Sepsis: A Benchmark MDP Built from Real Medical Data

Kartik Choudhary, Dhawal Gupta, Philip S. Thomas; 4:1546−1566, 2024.
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When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning

Claas A Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-massoud Farahmand; 4:1567−1597, 2024.
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ROER: Regularized Optimal Experience Replay

Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen; 4:1598−1618, 2024.
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Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL

Philipp Becker, Sebastian Mossburger, Fabian Otto, Gerhard Neumann; 4:1619−1655, 2024.
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RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference

Owen Oertell, Jonathan Daniel Chang, Yiyi Zhang, Kianté Brantley, Wen Sun; 4:1656−1673, 2024.
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A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo

Miguel Vasco, Takuma Seno, Kenta Kawamoto, Kaushik Subramanian, Peter R. Wurman, Peter Stone; 4:1674−1710, 2024.
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Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL

Miguel Suau, Matthijs T. J. Spaan, Frans A Oliehoek; 4:1711−1732, 2024.
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Learning Abstract World Models for Value-preserving Planning with Options

Rafael Rodriguez-Sanchez, George Konidaris; 4:1733−1758, 2024.
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Verification-Guided Shielding for Deep Reinforcement Learning

Davide Corsi, Guy Amir, Andoni Rodríguez, Guy Katz, César Sánchez, Roy Fox; 4:1759−1780, 2024.
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Learning Discrete World Models for Heuristic Search

Forest Agostinelli, Misagh Soltani; 4:1781−1792, 2024.
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Distributionally Robust Constrained Reinforcement Learning under Strong Duality

Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue; 4:1793−1821, 2024.
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Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations

Connor Mattson, Anurag Sidharth Aribandi, Daniel S. Brown; 4:1822−1840, 2024.
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Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning

Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood; 4:1841−1854, 2024.
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Policy-Guided Diffusion

Matthew Thomas Jackson, Michael Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob Nicolaus Foerster; 4:1855−1872, 2024.
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Agent-Centric Human Demonstrations Train World Models

James Staley, Elaine Short, Shivam Goel, Yash Shukla; 4:1873−1886, 2024.
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Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?

Akansha Kalra, Daniel S. Brown; 4:1887−1910, 2024.
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Imitation Learning from Observation through Optimal Transport

Wei-Di Chang, Scott Fujimoto, David Meger, Gregory Dudek; 4:1911−1923, 2024.
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Light-weight Probing of Unsupervised Representations for Reinforcement Learning

Wancong Zhang, Anthony GX-Chen, Vlad Sobal, Yann LeCun, Nicolas Carion; 4:1924−1949, 2024.
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Quantifying Interaction Level Between Agents Helps Cost-efficient Generalization in Multi-agent Reinforcement Learning

Yuxin Chen, Chen Tang, Thomas Tian, Chenran Li, Jinning Li, Masayoshi Tomizuka, Wei Zhan; 4:1950−1964, 2024.
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Shield Decomposition for Safe Reinforcement Learning in General Partially Observable Multi-Agent Environments

Daniel Melcer, Christopher Amato, Stavros Tripakis; 4:1965−1994, 2024.
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Reward Centering

Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton; 4:1995−2016, 2024.
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MultiHyRL: Robust Hybrid RL for Obstacle Avoidance against Adversarial Attacks on the Observation Space

Jan de Priester, Zachary Bell, Prashant Ganesh, Ricardo Sanfelice; 4:2017−2040, 2024.
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Investigating the Interplay of Prioritized Replay and Generalization

Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White; 5:2041−2058, 2024.
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Towards General Negotiation Strategies with End-to-End Reinforcement Learning

Bram M. Renting, Thomas M. Moerland, Holger Hoos, Catholijn M Jonker; 5:2059−2070, 2024.
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PID Accelerated Temporal Difference Algorithms

Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand; 5:2071−2095, 2024.
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States as goal-directed concepts: an epistemic approach to state-representation learning

Nadav Amir, Yael Niv, Angela J Langdon; 5:2096−2106, 2024.
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Posterior Sampling for Continuing Environments

Wanqiao Xu, Shi Dong, Benjamin Van Roy; 5:2107−2122, 2024.
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Reinforcement Learning from Delayed Observations via World Models

Armin Karamzade, Kyungmin Kim, Montek Kalsi, Roy Fox; 5:2123−2139, 2024.
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Offline Reinforcement Learning from Datasets with Structured Non-Stationarity

Johannes Ackermann, Takayuki Osa, Masashi Sugiyama; 5:2140−2161, 2024.
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Resource Usage Evaluation of Discrete Model-Free Deep Reinforcement Learning Algorithms

Olivia P. Dizon-Paradis, Stephen E. Wormald, Daniel E. Capecci, Avanti Bhandarkar, Damon L. Woodard; 5:2162−2177, 2024.
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D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning

Rafael Rafailov, Kyle Beltran Hatch, Anikait Singh, Aviral Kumar, Laura Smith, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip J. Ball, Jiajun Wu, Sergey Levine, Chelsea Finn; 5:2178−2197, 2024.
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Weight Clipping for Deep Continual and Reinforcement Learning

Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood; 5:2198−2217, 2024.
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A Batch Sequential Halving Algorithm without Performance Degradation

Sotetsu Koyamada, Soichiro Nishimori, Shin Ishii; 5:2218−2232, 2024.
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Causal Contextual Bandits with Adaptive Context

Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman; 5:2233−2263, 2024.
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Policy Architectures for Compositional Generalization in Control

Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran; 5:2264−2283, 2024.
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Semi-Supervised One Shot Imitation Learning

Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel; 5:2284−2297, 2024.
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Cross-environment Hyperparameter Tuning for Reinforcement Learning

Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White; 5:2298−2319, 2024.
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Human-compatible driving agents through data-regularized self-play reinforcement learning

Daphne Cornelisse, Eugene Vinitsky; 5:2320−2344, 2024.
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Inception: Efficiently Computable Misinformation Attacks on Markov Games

Jeremy McMahan, Young Wu, Yudong Chen, Jerry Zhu, Qiaomin Xie; 5:2345−2358, 2024.
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Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps

Linfeng Zhao, Lawson L.S. Wong; 5:2359−2372, 2024.
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Boosting Soft Q-Learning by Bounding

Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V Kulkarni; 5:2373−2399, 2024.
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Bandits with Multimodal Structure

Hassan SABER, Odalric-Ambrym Maillard; 5:2400−2439, 2024.
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Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning

Erin J Talvitie, Zilei Shao, Huiying Li, Jinghan Hu, Jacob Boerma, Rory Zhao, Xintong Wang; 5:2440−2460, 2024.
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Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms

Javad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh; 5:2461−2491, 2024.
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Optimizing Rewards while meeting $\omega$-regular Constraints

Christopher Zeitler, Kristina Miller, Sayan Mitra, John Schierman, Mahesh Viswanathan; 5:2492−2514, 2024.
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