David Abel

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 44

Top Topics

Mental Representations

Markov Decision Process

Reinforcement Learning

State Abstraction

Top Venues

Publications

Andi Peng, Yuying Sun, Tianmin Shu, David Abel
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. CoRR (2024)
David Abel, Mark K. Ho, Anna Harutyunyan
Three Dogmas of Reinforcement Learning. CoRR (2024)
Hyunin Lee, David Abel, Ming Jin, Javad Lavaei, Somayeh Sojoudi
A Black Swan Hypothesis in Markov Decision Process via Irrationality. CoRR (2024)
David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
On the Convergence of Bounded Agents. CoRR (2023)
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh
A Definition of Continual Reinforcement Learning. NeurIPS (2023)
Michael Bowling, John D. Martin, David Abel, Will Dabney
Settling the Reward Hypothesis. ICML (2023)
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh
A Definition of Continual Reinforcement Learning. CoRR (2023)
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)
Michael Bowling, John D. Martin, David Abel, Will Dabney
Settling the Reward Hypothesis. CoRR (2022)
David Abel
A Theory of Abstraction in Reinforcement Learning. CoRR (2022)
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
Meta-Gradients in Non-Stationary Environments. CoRR (2022)
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
Meta-Gradients in Non-Stationary Environments. CoLLAs (2022)
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
Lipschitz Lifelong Reinforcement Learning. AAAI (2021)
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 (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. CoRR (2021)
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 (2021)
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 (2021)
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)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. NeurIPS (2021)
David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman
Value Preserving State-Action Abstractions. AISTATS (2020)
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR (2020)
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup
What can I do here? A Theory of Affordances in Reinforcement Learning. ICML (2020)
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup
What can I do here? A Theory of Affordances in Reinforcement Learning. CoRR (2020)
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)
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
Lipschitz Lifelong Reinforcement Learning. CoRR (2020)
Kavosh Asadi, David Abel, Michael L. Littman
Learning State Abstractions for Transfer in Continuous Control. CoRR (2020)
Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris
Finding Options that Minimize Planning Time. ICML (2019)
David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong
State Abstraction as Compression in Apprenticeship Learning. AAAI (2019)
Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris
Discovering Options for Exploration by Minimizing Cover Time. CoRR (2019)
David Abel, John Winder, Marie desJardins, Michael L. Littman
The Expected-Length Model of Options. IJCAI (2019)
Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris
Discovering Options for Exploration by Minimizing Cover Time. ICML (2019)
David Abel
A Theory of State Abstraction for Reinforcement Learning. AAAI (2019)
David Abel
simple_rl: Reproducible Reinforcement Learning in Python. RML@ICLR (2019)
David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman
Bandit-Based Solar Panel Control. AAAI (2018)
Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris
Finding Options that Minimize Planning Time. CoRR (2018)
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 (2018)
David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman
State Abstractions for Lifelong Reinforcement Learning. ICML (2018)
David Abel, Yuu Jinnai, Sophie Yue Guo, George Dimitri Konidaris, Michael L. Littman
Policy and Value Transfer in Lifelong Reinforcement Learning. ICML (2018)
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
Latent Attention Networks. CoRR (2017)
David Abel, D. Ellis Hershkowitz, Michael L. Littman
Near Optimal Behavior via Approximate State Abstraction. CoRR (2017)
David Abel, John Salvatier, Andreas Stuhlmüller, Owain Evans
Agent-Agnostic Human-in-the-Loop Reinforcement Learning. CoRR (2017)
David Abel, D. Ellis Hershkowitz, Michael L. Littman
Near Optimal Behavior via Approximate State Abstraction. ICML (2016)
David Abel, James MacGlashan, Michael L. Littman
Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society (2016)
David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. CoRR (2016)
David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex
Goal-Based Action Priors. ICAPS (2015)
Gabriel Barth-Maron, David Abel, James MacGlashan, Stefanie Tellex
Affordances as Transferable Knowledge for Planning Agents. AAAI Fall Symposia (2014)