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On the Expressivity of Markov Reward


Nov 01, 2021
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh

* Accepted to NeurIPS 2021 

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Bad-Policy Density: A Measure of Reinforcement Learning Hardness


Oct 07, 2021
David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong

* Presented at the 2021 ICML Workshop on Reinforcement Learning Theory 

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Control of mental representations in human planning


May 14, 2021
Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths


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Revisiting Peng's Q($位$) for Modern Reinforcement Learning


Feb 27, 2021
Tadashi Kozuno, Yunhao Tang, Mark Rowland, R茅mi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

* 26 pages, 7 figures, 2 tables 

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What can I do here? A Theory of Affordances in Reinforcement Learning


Jun 26, 2020
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup

* Thirty-seventh International Conference on Machine Learning (ICML 2020) 

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The Efficiency of Human Cognition Reflects Planned Information Processing


Feb 13, 2020
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths

* 13 pg (incl. supplemental materials); included in Proceedings of the 34th AAAI Conference on Artificial Intelligence 

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Learning State Abstractions for Transfer in Continuous Control


Feb 08, 2020
Kavosh Asadi, David Abel, Michael L. Littman


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Lipschitz Lifelong Reinforcement Learning


Jan 17, 2020
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

* Submitted to ICML 2020, 21 pages, 15 figures 

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Discovering Options for Exploration by Minimizing Cover Time


Mar 16, 2019
Yuu Jinnai, Jee Won Park, David Abel, George Konidaris


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Mitigating Planner Overfitting in Model-Based Reinforcement Learning


Dec 03, 2018
Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman


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Finding Options that Minimize Planning Time


Oct 16, 2018
Yuu Jinnai, David Abel, Michael Littman, George Konidaris


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Modeling Latent Attention Within Neural Networks


Dec 30, 2017
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman


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Near Optimal Behavior via Approximate State Abstraction


Jan 15, 2017
David Abel, D. Ellis Hershkowitz, Michael L. Littman

* Earlier version published at ICML 2016 

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Agent-Agnostic Human-in-the-Loop Reinforcement Learning


Jan 15, 2017
David Abel, John Salvatier, Andreas Stuhlm眉ller, Owain Evans

* Presented at the NIPS Workshop on the Future of Interactive Learning Machines, 2016 

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Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains


Mar 14, 2016
David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire


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