Affordances as Transferable Knowledge for Planning Agents. - Gabriel Barth-Maron; David Abel; James MacGlashan; Stefanie Tellex (AAAI Fall Symposia, 2014)

Goal-Based Action Priors. - David Abel; D. Ellis Hershkowitz; Gabriel Barth-Maron; Stephen Brawner; Kevin O'Farrell; James MacGlashan; Stefanie Tellex (ICAPS, 2015)

Reinforcement Learning as a Framework for Ethical Decision Making. - David Abel; James MacGlashan; Michael L. Littman (AAAI Workshop: AI, Ethics, and Society, 2016)

Near Optimal Behavior via Approximate State Abstraction. - David Abel; D. Ellis Hershkowitz; Michael L. Littman (ICML, 2016)

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. - David Abel; Alekh Agarwal; Fernando Diaz 001; Akshay Krishnamurthy; Robert E. Schapire (CoRR, 2016)

Agent-Agnostic Human-in-the-Loop Reinforcement Learning. - David Abel; John Salvatier; Andreas Stuhlmüller; Owain Evans (CoRR, 2017)

Latent Attention Networks. - Christopher Grimm; Dilip Arumugam; Siddharth Karamcheti; David Abel; Lawson L. S. Wong; Michael L. Littman (CoRR, 2017)

Bandit-Based Solar Panel Control. - David Abel; Edward C. Williams; Stephen Brawner; Emily Reif; Michael L. Littman (AAAI, 2018)

State Abstractions for Lifelong Reinforcement Learning. - David Abel; Dilip Arumugam; Lucas Lehnert; Michael L. Littman (ICML, 2018)

Policy and Value Transfer in Lifelong Reinforcement Learning. - David Abel; Yuu Jinnai; Yue (Sophie) Guo; George Dimitri Konidaris; Michael L. Littman (ICML, 2018)

Finding Options that Minimize Planning Time. - Yuu Jinnai; David Abel; Michael L. Littman; George Dimitri Konidaris (CoRR, 2018)

Mitigating Planner Overfitting in Model-Based Reinforcement Learning. - Dilip Arumugam; David Abel; Kavosh Asadi; Nakul Gopalan; Christopher Grimm; Jun Ki Lee; Lucas Lehnert; Michael L. Littman (CoRR, 2018)

A Theory of State Abstraction for Reinforcement Learning. - David Abel (AAAI, 2019)

State Abstraction as Compression in Apprenticeship Learning. - David Abel; Dilip Arumugam; Kavosh Asadi; Yuu Jinnai; Michael L. Littman; Lawson L. S. Wong (AAAI, 2019)

simple_rl: Reproducible Reinforcement Learning in Python. - David Abel (RML@ICLR, 2019)

Finding Options that Minimize Planning Time. - Yuu Jinnai; David Abel; David Ellis Hershkowitz; Michael L. Littman; George Dimitri Konidaris (ICML, 2019)

Discovering Options for Exploration by Minimizing Cover Time. - Yuu Jinnai; Jee Won Park; David Abel; George Dimitri Konidaris (ICML, 2019)

The Expected-Length Model of Options. - David Abel; John Winder; Marie desJardins; Michael L. Littman (IJCAI, 2019)

People Do Not Just Plan, They Plan to Plan. - Mark K. Ho; David Abel; Jonathan D. Cohen 003; Michael L. Littman; Thomas L. Griffiths 001 (AAAI, 2020)

Value Preserving State-Action Abstractions. - David Abel; Nate Umbanhowar; Khimya Khetarpal; Dilip Arumugam; Doina Precup; Michael L. Littman (AISTATS, 2020)

What can I do here? A Theory of Affordances in Reinforcement Learning. - Khimya Khetarpal; Zafarali Ahmed; Gheorghe Comanici; David Abel; Doina Precup (ICML, 2020)

Learning State Abstractions for Transfer in Continuous Control. - Kavosh Asadi; David Abel; Michael Littman 002 (CoRR, 2020)

The Efficiency of Human Cognition Reflects Planned Information Processing. - Mark K. Ho; David Abel; Jonathan D. Cohen 003; Michael L. Littman; Thomas L. Griffiths 001 (CoRR, 2020)

A Theory of Abstraction in Reinforcement Learning. - David Abel (2020)

Lipschitz Lifelong Reinforcement Learning. - Erwan Lecarpentier; David Abel; Kavosh Asadi; Yuu Jinnai; Emmanuel Rachelson; Michael L. Littman (AAAI, 2021)

Revisiting Peng's Q(λ) for Modern Reinforcement Learning. - Tadashi Kozuno; Yunhao Tang; Mark Rowland 001; Rémi Munos; Steven Kapturowski; Will Dabney; Michal Valko; David Abel (ICML, 2021)

On the Expressivity of Markov Reward. - David Abel; Will Dabney; Anna Harutyunyan; Mark K. Ho; Michael L. Littman; Doina Precup; Satinder Singh 001 (NeurIPS, 2021)

Control of mental representations in human planning. - Mark K. Ho; David Abel; Carlos G. Correa; Michael L. Littman; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (CoRR, 2021)

Bad-Policy Density: A Measure of Reinforcement Learning Hardness. - David Abel; Cameron Allen; Dilip Arumugam; D. Ellis Hershkowitz; Michael L. Littman; Lawson L. S. Wong (CoRR, 2021)

Meta-Gradients in Non-Stationary Environments. - Jelena Luketina; Sebastian Flennerhag; Yannick Schroecker; David Abel; Tom Zahavy; Satinder Singh 001 (CoLLAs, 2022)

On the Expressivity of Markov Reward (Extended Abstract). - David Abel; Will Dabney; Anna Harutyunyan; Mark K. Ho; Michael L. Littman; Doina Precup; Satinder Singh 001 (IJCAI, 2022)

A Theory of Abstraction in Reinforcement Learning. - David Abel (CoRR, 2022)

Settling the Reward Hypothesis. - Michael Bowling; John D. Martin; David Abel; Will Dabney (ICML, 2023)

A Definition of Continual Reinforcement Learning. - David Abel; André Barreto 001; Benjamin Van Roy; Doina Precup; Hado Philip van Hasselt; Satinder Singh 001 (NeurIPS, 2023)

On the Convergence of Bounded Agents. - David Abel; André Barreto 001; Hado van Hasselt; Benjamin Van Roy; Doina Precup; Satinder Singh 001 (CoRR, 2023)

Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input. - Andi Peng; Yuying Sun; Tianmin Shu; David Abel (ICML, 2024)

Three Dogmas of Reinforcement Learning. - David Abel; Mark K. Ho; Anna Harutyunyan (RLJ, 2024)

A Black Swan Hypothesis in Markov Decision Process via Irrationality. - Hyunin Lee; David Abel; Ming Jin 002; Javad Lavaei; Somayeh Sojoudi (CoRR, 2024)

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 (ICLR, 2025)

A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety. - Hyunin Lee; Chanwoo Park; David Abel; Ming Jin 002 (ICLR, 2025)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Agency Is Frame-Dependent. - David Abel; André Barreto 001; Michael Bowling; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Plasticity as the Mirror of Empowerment. - David Abel; Michael Bowling; André Barreto 001; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

General agents need world models. - Jonathan Richens; David Abel; Alexis Bellot; Tom Everitt (CoRR, 2025)

Memory Allocation in Resource-Constrained Reinforcement Learning. - Massimiliano Tamborski; David Abel (CoRR, 2025)

Remembering the Markov Property in Cooperative MARL. - Kale-ab Abebe Tessera; Leonard Hinckeldey; Riccardo Zamboni; David Abel; Amos Storkey (CoRR, 2025)