Posters sessions for a paper are the same day as the presentation day.

Aug 10, Oral Track 1: Evaluation - Room 168

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
Harnessing Discrete Representations for Continual Reinforcement Learning

Edan Jacob Meyer, Adam White, Marlos C. Machado
Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning

Davide Corsi, Davide Camponogara, Alessandro Farinelli
Investigating the Interplay of Prioritized Replay and Generalization

Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White
ICU-Sepsis: A Benchmark MDP Built from Real Medical Data

Kartik Choudhary, Dhawal Gupta, Philip S. Thomas
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
OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

Quentin Delfosse, Jannis Blüml, Bjarne Gregori, Sebastian Sztwiertnia, Kristian Kersting
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning

Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks

Antonin Raffin, Olivier Sigaud, Jens Kober, Alin Albu-Schaeffer, João Silvério, Freek Stulp
Combining Automated Optimisation of Hyperparameters and Reward Shape

Julian Dierkes, Emma Cramer, Holger Hoos, Sebastian Trimpe
Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning

Marcel Hussing, Jorge Mendez-Mendez, Anisha Singrodia, Cassandra Kent, Eric Eaton
Stable-Baselines3: Reliable Reinforcement Learning Implementations

Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann

Aug 10, Oral Track 2: Theoretical RL and bandit algorithms - Room 165/169

A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning

Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli
Bandits with Multimodal Structure

Hassan SABER, Odalric-Ambrym Maillard
Policy Gradient with Active Importance Sampling

Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

He Wang, Laixi Shi, Yuejie Chi
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits

Woojin Jeong, Seungki Min
A Batch Sequential Halving Algorithm without Performance Degradation

Sotetsu Koyamada, Soichiro Nishimori, Shin Ishii
Graph Neural Thompson Sampling

Shuang Wu, Arash A. Amini
A Tighter Convergence Proof of Reverse Experience Replay

Nan Jiang, Jinzhao Li, Yexiang Xue
Cost Aware Best Arm Identification

Kellen Kanarios, Qining Zhang, Lei Ying
Towards Principled, Practical Policy Gradient for Bandits and Tabular MDPs

Michael Lu, Matin Aghaei, Anant Raj, Sharan Vaswani
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
Causal Contextual Bandits with Adaptive Context

Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman
Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch

Shangtong Zhang, Remi Tachet des Combes, Romain Laroche

Aug 10, Oral Track 3: Multi-agent RL and planning algorithms - Room 174/176

Co-Learning Empirical Games & World Models

Max Olan Smith, Michael P. Wellman
Best Response Shaping

Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron Courville
Shield Decomposition for Safe Reinforcement Learning in General Partially Observable Multi-Agent Environments

Daniel Melcer, Christopher Amato, Stavros Tripakis
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
Reinforcement Learning from Delayed Observations via World Models

Armin Karamzade, Kyungmin Kim, Montek Kalsi, Roy Fox
Cyclicity-Regularized Coordination Graphs

Oliver Järnefelt, Mahdi Kallel, Carlo D'Eramo
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization

Aditya Kapoor, Benjamin Freed, Jeff Schneider, Howie Choset
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems

Ho Long Fung, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Inception: Efficiently Computable Misinformation Attacks on Markov Games

Jeremy McMahan, Young Wu, Yudong Chen, Jerry Zhu, Qiaomin Xie
Human-compatible driving agents through data-regularized self-play reinforcement learning

Daphne Cornelisse, Eugene Vinitsky
On Welfare-Centric Fair Reinforcement Learning

Cyrus Cousins, Kavosh Asadi, Elita Lobo, Michael Littman
BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations

Robert J. Moss, Anthony Corso, Jef Caers, Mykel Kochenderfer

Aug 10, Oral Track 4: Deep reinforcement learning - Room 163

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Marcel Hussing, Claas A Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, Eric Eaton
Mixture of Experts in a Mixture of RL settings

Timon Willi, Johan Samir Obando Ceron, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Pablo Samuel Castro
Light-weight Probing of Unsupervised Representations for Reinforcement Learning

Wancong Zhang, Anthony GX-Chen, Vlad Sobal, Yann LeCun, Nicolas Carion
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace

Léopold Maytié, Benjamin Devillers, Alexandre Arnold, Rufin VanRullen
PASTA: Pretrained Action-State Transformer Agents

Raphael Boige, Yannis Flet-Berliac, Lars C.P.M. Quaedvlieg, Arthur Flajolet, Guillaume Richard, Thomas PIERROT
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL

Philipp Becker, Sebastian Mossburger, Fabian Otto, Gerhard Neumann
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning

Abdulaziz Almuzairee, Nicklas Hansen, Henrik I Christensen
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
Policy-Guided Diffusion

Matthew Thomas Jackson, Michael Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob Nicolaus Foerster
SplAgger: Split Aggregation for Meta-Reinforcement Learning

Jacob Beck, Matthew Thomas Jackson, Risto Vuorio, Zheng Xiong, Shimon Whiteson
Learning to Optimize for Reinforcement Learning

Qingfeng Lan, A. Rupam Mahmood, Shuicheng YAN, Zhongwen Xu
Investigating the properties of neural network representations in reinforcement learning

Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters

Aug 11, Oral Track 1: RL from human feedback and imitation learning - Room 168

Learning Action-based Representations Using Invariance

Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang
Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations

Connor Mattson, Anurag Sidharth Aribandi, Daniel S. Brown
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity

Johannes Ackermann, Takayuki Osa, Masashi Sugiyama
Offline Diversity Maximization under Imitation Constraints

Marin Vlastelica, Jin Cheng, Georg Martius, Pavel Kolev
Imitation Learning from Observation through Optimal Transport

Wei-Di Chang, Scott Fujimoto, David Meger, Gregory Dudek
ROIL: Robust Offline Imitation Learning without Trajectories

Gersi Doko, Guang Yang, Daniel S. Brown, Marek Petrik
Agent-Centric Human Demonstrations Train World Models

James Staley, Elaine Short, Shivam Goel, Yash Shukla
Inverse Reinforcement Learning with Multiple Planning Horizons

Jiayu Yao, Weiwei Pan, Finale Doshi-Velez, Barbara E Engelhardt
Semi-Supervised One Shot Imitation Learning

Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis

Qining Zhang, Honghao Wei, Lei Ying
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning

Nicholas E. Corrado, Yuxiao Qu, John U. Balis, Adam Labiosa, Josiah P. Hanna
Reward (Mis)design for autonomous driving☆

W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, Peter Stone
Models of human preference for learning reward functions

W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro G Allievi

Aug 11, Oral Track 2: Foundations - Room 165/169

The Cliff of Overcommitment with Policy Gradient Step Sizes

Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas
Demystifying the Recency Heuristic in Temporal-Difference Learning

Brett Daley, Marlos C. Machado, Martha White
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning

Claas A Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-massoud Farahmand
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage

Kevin Tan, Ziping Xu
States as goal-directed concepts: an epistemic approach to state-representation learning

Nadav Amir, Yael Niv, Angela J Langdon
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior

Zhiyuan Zhou, Shreyas Sundara Raman, Henry Sowerby, Michael Littman
Unifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets

Benjamin Freed, Thomas Wei, Roberto Calandra, Jeff Schneider, Howie Choset
Multistep Inverse Is Not All You Need

Alexander Levine, Peter Stone, Amy Zhang
An Idiosyncrasy of Time-discretization in Reinforcement Learning

Kris De Asis, Richard S. Sutton
Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL

Miguel Suau, Matthijs T. J. Spaan, Frans A Oliehoek
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
Structure in Deep Reinforcement Learning: A Survey and Open Problems

Aditya Mohan, Amy Zhang, Marius Lindauer

Aug 11, Oral Track 3: Applied reinforcement learning - Room 174/176

Sequential Decision-Making for Inline Text Autocomplete

Rohan Chitnis, Shentao Yang, Alborz Geramifard
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
Towards General Negotiation Strategies with End-to-End Reinforcement Learning

Bram M. Renting, Thomas M. Moerland, Holger Hoos, Catholijn M Jonker
JoinGym: An Efficient Join Order Selection Environment

Junxiong Wang, Kaiwen Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
Policy Architectures for Compositional Generalization in Control

Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran
Verification-Guided Shielding for Deep Reinforcement Learning

Davide Corsi, Guy Amir, Andoni Rodríguez, Guy Katz, César Sánchez, Roy Fox
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning

Zakariae EL ASRI, Olivier Sigaud, Nicolas THOME
Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies

Yu Luo, Fuchun Sun, Tianying Ji, Xianyuan Zhan
RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference

Owen Oertell, Jonathan Daniel Chang, Yiyi Zhang, Kianté Brantley, Wen Sun
Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps

Linfeng Zhao, Lawson L.S. Wong
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning

Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood
Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras

Mhairi Dunion, Stefano V Albrecht
Emergent behaviour and neural dynamics in artificial agents tracking odour plumes

Satpreet H. Singh, Floris van Breugel, Rajesh P. N. Rao, Bingni W. Brunton
GVFs in the real world: making predictions online for water treatment

Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White

Aug 11, Oral Track 4: RL algorithms - Room 163

Weight Clipping for Deep Continual and Reinforcement Learning

Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes

Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe, Peinan Zhang
ROER: Regularized Optimal Experience Replay

Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen
Learning Discrete World Models for Heuristic Search

Forest Agostinelli, Misagh Soltani
Boosting Soft Q-Learning by Bounding

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

Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton
Stabilizing Extreme Q-learning by Maclaurin Expansion

Motoki Omura, Takayuki Osa, YUSUKE Mukuta, Tatsuya Harada
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors

Emma Cramer, Bernd Frauenknecht, Ramil Sabirov, Sebastian Trimpe
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization

Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart
PID Accelerated Temporal Difference Algorithms

Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand
SwiftTD: A Fast and Robust Algorithm for Temporal Difference Learning

Khurram Javed, Arsalan Sharifnassab, Richard S. Sutton
Posterior Sampling for Continuing Environments

Wanqiao Xu, Shi Dong, Benjamin Van Roy
Off-Policy Actor-Critic with Emphatic Weightings

Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White

Aug 12, Oral Track 1: Social and economic aspects - Room 168

Value Internalization: Learning and Generalizing from Social Reward

Frieda Rong, Max Kleiman-Weiner
Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?

Akansha Kalra, Daniel S. Brown
Three Dogmas of Reinforcement Learning

David Abel, Mark K Ho, Anna Harutyunyan
MultiHyRL: Robust Hybrid RL for Obstacle Avoidance against Adversarial Attacks on the Observation Space

Jan de Priester, Zachary Bell, Prashant Ganesh, Ricardo Sanfelice
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
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning

Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi

Aug 12, Oral Track 2: Theoretical RL - Room 165/169

The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation

Noah Golowich, Ankur Moitra
Optimizing Rewards while meeting $\omega$-regular Constraints

Christopher Zeitler, Kristina Miller, Sayan Mitra, John Schierman, Mahesh Viswanathan
Distributionally Robust Constrained Reinforcement Learning under Strong Duality

Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue
Non-adaptive Online Finetuning for Offline Reinforcement Learning

Audrey Huang, Mohammad Ghavamzadeh, Nan Jiang, Marek Petrik
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation

Yixuan Zhang, Qiaomin Xie
An Optimal Tightness Bound for the Simulation Lemma

Sam Lobel, Ronald Parr
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning

Aritra Mitra, George J. Pappas, Hamed Hassani

Aug 12, Oral Track 3: Exploration - Room 174/176

The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough

Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning

Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed, Glen Berseth
Exploring Uncertainty in Distributional Reinforcement Learning

Georgy Antonov, Peter Dayan
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
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning

Trevor McInroe, Adam Jelley, Stefano V Albrecht, Amos Storkey
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance

Jakob Hollenstein, Sayantan Auddy, Matteo Saveriano, Erwan Renaudo, Justus Piater

Aug 12, Oral Track 4: Hierarchical RL and planning algorithms - Room 163

Online Planning in POMDPs with State-Requests

Raphaël Avalos, Eugenio Bargiacchi, Ann Nowe, Diederik Roijers, Frans A Oliehoek
Informed POMDP: Leveraging Additional Information in Model-Based RL

Gaspard Lambrechts, Adrien Bolland, Damien Ernst
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
Dreaming of Many Worlds: Learning Contextual World Models aids Zero-Shot Generalization

Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp
Learning Abstract World Models for Value-preserving Planning with Options

Rafael Rodriguez-Sanchez, George Konidaris
Granger Causal Interaction Skill Chains

Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

Philipp Becker, Gerhard Neumann
Mitigating Value Hallucination in Dyna-Style Planning via Multistep Predecessor Models

Farzane Aminmansour, Taher Jafferjee, Ehsan Imani, Erin J. Talvitie, Michael Bowling, Martha White

RLC 2024: Papers (rl-conference.cc)