David Abel
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
22
Venues
5
Active years
2016–2025
Best venue rank
A*
Where they publish
Papers
22 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning. | Samuel Garcin, Trevor McInroe, Pablo Samuel Castro, Christopher G. Lucas, David Abel, Prakash Panangaden, Stefano V. Albrecht |
| 2025 | ICLR | A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety. | Hyunin Lee, Chanwoo Park, David Abel, Ming Jin |
| 2025 | ICML | General agents need world models. | Jonathan Richens, Tom Everitt, David Abel |
| 2024 | ICML | Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. | Andi Peng, Yuying Sun, Tianmin Shu, David Abel |
| 2023 | ICML | Settling the Reward Hypothesis. | Michael Bowling, John D. Martin, David Abel, Will Dabney |
| 2022 | IJCAI | On the Expressivity of Markov Reward (Extended Abstract). | David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh |
| 2021 | AAAI | Lipschitz Lifelong Reinforcement Learning. | Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman |
| 2021 | ICML | Revisiting Peng's Q(λ) for Modern Reinforcement Learning. | Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rmi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel |
| 2020 | AAAI | People Do Not Just Plan, They Plan to Plan. | Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths |
| 2020 | AISTATS | Value Preserving State-Action Abstractions. | David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman |
| 2020 | ICML | What can I do here? A Theory of Affordances in Reinforcement Learning. | Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup |
| 2019 | AAAI | A Theory of State Abstraction for Reinforcement Learning. | David Abel |
| 2019 | AAAI | State Abstraction as Compression in Apprenticeship Learning. | David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong |
| 2019 | ICLR | simple_rl: Reproducible Reinforcement Learning in Python. | David Abel |
| 2019 | ICML | Finding Options that Minimize Planning Time. | Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris |
| 2019 | ICML | Discovering Options for Exploration by Minimizing Cover Time. | Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris |
| 2019 | IJCAI | The Expected-Length Model of Options. | David Abel, John Winder, Marie desJardins, Michael L. Littman |
| 2018 | AAAI | Bandit-Based Solar Panel Control. | David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman |
| 2018 | ICML | State Abstractions for Lifelong Reinforcement Learning. | David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman |
| 2018 | ICML | Policy and Value Transfer in Lifelong Reinforcement Learning. | David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman |
| 2016 | AAAI | Reinforcement Learning as a Framework for Ethical Decision Making. | David Abel, James MacGlashan, Michael L. Littman |
| 2016 | ICML | Near Optimal Behavior via Approximate State Abstraction. | David Abel, D. Ellis Hershkowitz, Michael L. Littman |