David Abel - RankMe

65 papers A* 22A 1C 1Journal 37Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas. CoRR Alper Demir, Hüseyin Aydin, Kale-ab Abebe Tessera, David Abel, Stefano V. Albrecht
2026	J	`jnl`	Probing Dec-POMDP Reasoning in Cooperative MARL. CoRR Kale-ab Abebe Tessera, Leonard Hinckeldey, Riccardo Zamboni, David Abel, Amos J. Storkey
2025	A*	`conf`	A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety. ICLR Hyunin Lee, Chanwoo Park, David Abel, Ming Jin
2025	J	`jnl`	Agency Is Frame-Dependent. CoRR David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	J	`jnl`	Discovering Coordinated Joint Options via Inter-Agent Relative Dynamics. CoRR Raul D. Steleac, Mohan Sridharan, David Abel
2025	J	`jnl`	Enhancing Tactile-based Reinforcement Learning for Robotic Control. CoRR Elle Miller, Trevor McInroe, David Abel, Oisin Mac Aodha, Sethu Vijayakumar
2025	J	`jnl`	Forgetting is Everywhere. CoRR Ben Sanati, Thomas L. Lee, Trevor McInroe, Aidan Scannell, Nikolay Malkin, David Abel, Amos Storkey
2025	A*	`conf`	General agents need world models. ICML Jonathan Richens, Tom Everitt, David Abel
2025	J	`jnl`	General agents need world models. CoRR Jonathan Richens, David Abel, Alexis Bellot, Tom Everitt
2025	J	`jnl`	Memory Allocation in Resource-Constrained Reinforcement Learning. CoRR Massimiliano Tamborski, David Abel
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. CoRR Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. J. Mach. Learn. Res. Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Plasticity as the Mirror of Empowerment. CoRR David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	J	`jnl`	Remembering the Markov Property in Cooperative MARL. CoRR Kale-ab Abebe Tessera, Leonard Hinckeldey, Riccardo Zamboni, David Abel, Amos Storkey
2025	A*	`conf`	Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning. ICLR Samuel Garcin, Trevor McInroe, Pablo Samuel Castro, Christopher G. Lucas, David Abel, Prakash Panangaden, Stefano V. Albrecht
2025	J	`jnl`	Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning. CoRR Samuel Garcin, Trevor McInroe, Pablo Samuel Castro, Prakash Panangaden, Christopher G. Lucas, David Abel, Stefano V. Albrecht
2024	J	`jnl`	A Black Swan Hypothesis in Markov Decision Process via Irrationality. CoRR Hyunin Lee, David Abel, Ming Jin, Javad Lavaei, Somayeh Sojoudi
2024	A*	`conf`	Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. ICML Andi Peng, Yuying Sun, Tianmin Shu, David Abel
2024	J	`jnl`	Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. CoRR Andi Peng, Yuying Sun, Tianmin Shu, David Abel
2024	J	`jnl`	Three Dogmas of Reinforcement Learning. RLJ David Abel, Mark K. Ho, Anna Harutyunyan
2024	J	`jnl`	Three Dogmas of Reinforcement Learning. CoRR David Abel, Mark K. Ho, Anna Harutyunyan
2023	A*	`conf`	A Definition of Continual Reinforcement Learning. NeurIPS David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh
2023	J	`jnl`	A Definition of Continual Reinforcement Learning. CoRR David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh
2023	J	`jnl`	On the Convergence of Bounded Agents. CoRR David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
2023	A*	`conf`	Settling the Reward Hypothesis. ICML Michael Bowling, John D. Martin, David Abel, Will Dabney
2022	J	`jnl`	A Theory of Abstraction in Reinforcement Learning. CoRR David Abel
2022	—	`conf`	Meta-Gradients in Non-Stationary Environments. CoLLAs Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
2022	J	`jnl`	Meta-Gradients in Non-Stationary Environments. CoRR Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
2022	A*	`conf`	On the Expressivity of Markov Reward (Extended Abstract). IJCAI David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2022	J	`jnl`	Settling the Reward Hypothesis. CoRR Michael Bowling, John D. Martin, David Abel, Will Dabney
2021	J	`jnl`	Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong
2021	J	`jnl`	Control of mental representations in human planning. CoRR Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths
2021	A*	`conf`	Lipschitz Lifelong Reinforcement Learning. AAAI Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
2021	A*	`conf`	On the Expressivity of Markov Reward. NeurIPS David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	J	`jnl`	On the Expressivity of Markov Reward. CoRR David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	A*	`conf`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	J	`jnl`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2020	—	`—`	A Theory of Abstraction in Reinforcement Learning. David Abel
2020	J	`jnl`	Learning State Abstractions for Transfer in Continuous Control. CoRR Kavosh Asadi, David Abel, Michael Littman
2020	J	`jnl`	Lipschitz Lifelong Reinforcement Learning. CoRR Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
2020	A*	`conf`	People Do Not Just Plan, They Plan to Plan. AAAI Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
2020	J	`jnl`	The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
2020	A	`conf`	Value Preserving State-Action Abstractions. AISTATS David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman
2020	A*	`conf`	What can I do here? A Theory of Affordances in Reinforcement Learning. ICML Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup
2020	J	`jnl`	What can I do here? A Theory of Affordances in Reinforcement Learning. CoRR Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup
2019	A*	`conf`	A Theory of State Abstraction for Reinforcement Learning. AAAI David Abel
2019	A*	`conf`	Discovering Options for Exploration by Minimizing Cover Time. ICML Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris
2019	J	`jnl`	Discovering Options for Exploration by Minimizing Cover Time. CoRR Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris
2019	A*	`conf`	Finding Options that Minimize Planning Time. ICML Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris
2019	A*	`conf`	State Abstraction as Compression in Apprenticeship Learning. AAAI David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong
2019	A*	`conf`	The Expected-Length Model of Options. IJCAI David Abel, John Winder, Marie desJardins, Michael L. Littman
2019	—	`conf`	simple_rl: Reproducible Reinforcement Learning in Python. RML@ICLR David Abel
2018	A*	`conf`	Bandit-Based Solar Panel Control. AAAI David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman
2018	J	`jnl`	Finding Options that Minimize Planning Time. CoRR Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris
2018	J	`jnl`	Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman
2018	A*	`conf`	Policy and Value Transfer in Lifelong Reinforcement Learning. ICML David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman
2018	A*	`conf`	State Abstractions for Lifelong Reinforcement Learning. ICML David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman
2017	J	`jnl`	Agent-Agnostic Human-in-the-Loop Reinforcement Learning. CoRR David Abel, John Salvatier, Andreas Stuhlmüller, Owain Evans
2017	J	`jnl`	Latent Attention Networks. CoRR Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
2017	J	`jnl`	Near Optimal Behavior via Approximate State Abstraction. CoRR David Abel, D. Ellis Hershkowitz, Michael L. Littman
2016	J	`jnl`	Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. CoRR David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
2016	A*	`conf`	Near Optimal Behavior via Approximate State Abstraction. ICML David Abel, D. Ellis Hershkowitz, Michael L. Littman
2016	C	`conf`	Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society David Abel, James MacGlashan, Michael L. Littman
2015	A*	`conf`	Goal-Based Action Priors. ICAPS David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex
2014	—	`conf`	Affordances as Transferable Knowledge for Planning Agents. AAAI Fall Symposia Gabriel Barth-Maron, David Abel, James MacGlashan, Stefanie Tellex