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ORCIDDavid Abel
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2020 – today
- 2024
[j1]David Abel, Mark K. Ho, Anna Harutyunyan:
Three Dogmas of Reinforcement Learning. RLJ 2: 629-644 (2024)- [c24]Andi Peng, Yuying Sun, Tianmin Shu, David Abel:
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. ICML 2024 - [i23]Andi Peng, Yuying Sun, Tianmin Shu, David Abel:
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. CoRR abs/2405.14769 (2024) - [i22]David Abel, Mark K. Ho, Anna Harutyunyan:
Three Dogmas of Reinforcement Learning. CoRR abs/2407.10583 (2024) - [i21]Hyunin Lee, David Abel, Ming Jin, Javad Lavaei, Somayeh Sojoudi:
A Black Swan Hypothesis in Markov Decision Process via Irrationality. CoRR abs/2407.18422 (2024) - 2023
- [c23]Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. ICML 2023: 3003-3020 - [c22]David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh:
A Definition of Continual Reinforcement Learning. NeurIPS 2023 - [i20]David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh:
On the Convergence of Bounded Agents. CoRR abs/2307.11044 (2023) - [i19]David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh:
A Definition of Continual Reinforcement Learning. CoRR abs/2307.11046 (2023) - 2022
- [c21]Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh:
Meta-Gradients in Non-Stationary Environments. CoLLAs 2022: 886-901 - [c20]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward (Extended Abstract). IJCAI 2022: 5254-5258 - [i18]David Abel:
A Theory of Abstraction in Reinforcement Learning. CoRR abs/2203.00397 (2022) - [i17]Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh:
Meta-Gradients in Non-Stationary Environments. CoRR abs/2209.06159 (2022) - [i16]Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. CoRR abs/2212.10420 (2022) - 2021
- [c19]Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman:
Lipschitz Lifelong Reinforcement Learning. AAAI 2021: 8270-8278 - [c18]Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel:
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML 2021: 5794-5804 - [c17]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. NeurIPS 2021: 7799-7812 - [i15]Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel:
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR abs/2103.00107 (2021) - [i14]Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths:
Control of mental representations in human planning. CoRR abs/2105.06948 (2021) - [i13]David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong:
Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR abs/2110.03424 (2021) - [i12]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. CoRR abs/2111.00876 (2021) - 2020
- [b1]David Abel:
A Theory of Abstraction in Reinforcement Learning. Brown University, USA, 2020 - [c16]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
People Do Not Just Plan, They Plan to Plan. AAAI 2020: 1300-1307 - [c15]David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman:
Value Preserving State-Action Abstractions. AISTATS 2020: 1639-1650 - [c14]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. ICML 2020: 5243-5253 - [i11]Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman:
Lipschitz Lifelong Reinforcement Learning. CoRR abs/2001.05411 (2020) - [i10]Kavosh Asadi, David Abel, Michael Littman:
Learning State Abstractions for Transfer in Continuous Control. CoRR abs/2002.05518 (2020) - [i9]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR abs/2002.05769 (2020) - [i8]Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup:
What can I do here? A Theory of Affordances in Reinforcement Learning. CoRR abs/2006.15085 (2020)
2010 – 2019
- 2019
- [c13]David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong:
State Abstraction as Compression in Apprenticeship Learning. AAAI 2019: 3134-3142 - [c12]David Abel:
A Theory of State Abstraction for Reinforcement Learning. AAAI 2019: 9876-9877 - [c11]David Abel:
simple_rl: Reproducible Reinforcement Learning in Python. RML@ICLR 2019 - [c10]Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris:
Finding Options that Minimize Planning Time. ICML 2019: 3120-3129 - [c9]Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris:
Discovering Options for Exploration by Minimizing Cover Time. ICML 2019: 3130-3139 - [c8]David Abel, John Winder, Marie desJardins, Michael L. Littman:
The Expected-Length Model of Options. IJCAI 2019: 1951-1958 - [i7]Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris:
Discovering Options for Exploration by Minimizing Cover Time. CoRR abs/1903.00606 (2019) - 2018
- [c7]David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman:
Bandit-Based Solar Panel Control. AAAI 2018: 7713-7718 - [c6]David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman:
State Abstractions for Lifelong Reinforcement Learning. ICML 2018: 10-19 - [c5]David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman:
Policy and Value Transfer in Lifelong Reinforcement Learning. ICML 2018: 20-29 - [i6]Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris:
Finding Options that Minimize Planning Time. CoRR abs/1810.07311 (2018) - [i5]Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman:
Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR abs/1812.01129 (2018) - 2017
- [i4]David Abel, John Salvatier, Andreas Stuhlmüller, Owain Evans:
Agent-Agnostic Human-in-the-Loop Reinforcement Learning. CoRR abs/1701.04079 (2017) - [i3]David Abel, D. Ellis Hershkowitz, Michael L. Littman:
Near Optimal Behavior via Approximate State Abstraction. CoRR abs/1701.04113 (2017) - [i2]Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman:
Latent Attention Networks. CoRR abs/1706.00536 (2017) - 2016
- [c4]David Abel, James MacGlashan, Michael L. Littman:
Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society 2016 - [c3]David Abel, D. Ellis Hershkowitz, Michael L. Littman:
Near Optimal Behavior via Approximate State Abstraction. ICML 2016: 2915-2923 - [i1]David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire:
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. CoRR abs/1603.04119 (2016) - 2015
- [c2]David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex:
Goal-Based Action Priors. ICAPS 2015: 306-314 - 2014
- [c1]Gabriel Barth-Maron, David Abel, James MacGlashan, Stefanie Tellex:
Affordances as Transferable Knowledge for Planning Agents. AAAI Fall Symposia 2014
Coauthor Index
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