Thinking is Another Form of Control. Josiah P. Hanna and Nicholas E. Corrado. 

• Awarded: Most Thought-provoking paper.

Analogy making as amortised model construction. David G Nagy, Tingke Shen, Hanqi Zhou, Charley M Wu, and Peter Dayan.

Agent-centric learning: from external reward maximization to internal knowledge curation. Hanqi Zhou, Fryderyk Mantiuk, David G Nagy, and Charley M Wu.

Budget-Aware Feature Selection for Reinforcement Learning. Ofek Glick, Argaman Mordoch, Guy Azran, and Sarah Keren.

A Clean Slate for Offline Reinforcement Learning. Matthew Thomas Jackson, Uljad Berdica, Jarek Luca Liesen, Shimon Whiteson,  and Jakob Nicolaus Foerster.

Is Exploration or Optimization the Problem for Deep Reinforcement Learning? Glen Berseth.

Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective. Larry Preuett, Qiuyi Zhang, and Muhammad Aurangzeb Ahmad

Distribution Parameter Actor-Critic: Shifting the Agent-Environment Boundary for Diverse Action Spaces. Jiamin He, A. Rupam Mahmood, and Martha White.

Off by a Beat: Temporal Misalignment in Offline RL for Healthcare. Shengpu Tang, Jiayu Yao, Jenna Wiens, and Sonali Parbhoo.

Reconciling Set-Valued Policy & Dead-End Discovery in RL: An Empirical Analysis. Sixing Wu and Shengpu Tang.

Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning. Théo Vincent, Yogesh Tripathi, Tim Faust, Yaniv Oren, Jan Peters, and Carlo D'Eramo.

Thinking is Another Form of Control. Josiah P. Hanna and Nicholas E. Corrado.

A Geometric Lens on RL Environment Complexity Based on Ricci Curvature.  Ali Saheb Pasand, Pablo Samuel Castro, and Pouya Bashivan.

Which Rewards Matter? Reward Selection for Reinforcement Learning from Limited Feedback. Shreyas Chaudhari, Renhao Zhang, Philip S. Thomas, and Bruno Castro da Silva.

A Unified MDP Framework for Solving Robust, Convex, Multi-Discount Constraints, and Beyond. Toshinori Kitamura, Arnob Ghosh, Tadashi Kozuno, Kenta Hoshino, Yohei Hosoe, Kazumi Kasaura, Wataru Kumagai, Paavo Parmas, and Yutaka Matsuo.

What Matters for Maximizing Data Reuse In Value-based Deep Reinforcement Learning. Roger Creus Castanyer, Glen Berseth, and Pablo Samuel Castro.

A perspective on fluid mechanical environments for challenges in reinforcement learning. Shruti Mishra, Michael Chang, Vamsi Spandan Arza, and Shmuel Rubinstein.

Putting the Spotlight on the Initial State Distribution. Aditya Makkar, Aarshvi Gajjar, and Eugene Vinitsky.

Learning Context-Sensitive State and Action Abstractions for Reinforcement Learning with Parameterized Actions. Rashmeet Kaur Nayyar, Naman Shah, and Siddharth Srivastava.

On Temporal Credit Assignment and Data-Efficient Reinforcement Learning. Dilip Arumugam and Thomas L. Griffiths.

Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learning. Sid Bharthulwar, Stone Tao, and Hao Su.

On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling. Nicholas E. Corrado and Josiah P. Hanna.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals. Yannick Molinghen and Tom Lenaerts.

General Value Discrepancies Mitigate Partial Observability in Reinforcement Learning. Peter Koepernik, Ruo Yu Tao, Ronald Parr, George Konidaris, and Cameron Allen.

Reframing Multi-Agent Reinforcement Learning with Variational Inequalities. Baraah A. M. Sidahmed, and Tatjana Chavdarova.

Remembering the Markov Property in Cooperative MARL. Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni, David Abel, and Amos Storkey.

Learning What to Remember for Non-Markovian Reinforcement Learning. Geraud Nangue Tasse, Matthew Riemer, Benjamin Rosman, and Tim Klinger.

lluminating the three dogmas of reinforcement learning under evolutionary light. Mani Hamidi and Terrence Deacon.