Follow
David Abel
David Abel
Research Scientist, DeepMind
Verified email at deepmind.com - Homepage
Title
Cited by
Cited by
Year
Near optimal behavior via approximate state abstraction
D Abel, DE Hershkowitz, ML Littman
International Conference on Machine Learning, 2915--2923, 2016
1872016
Reinforcement learning as a framework for ethical decision making
D Abel, J MacGlashan, ML Littman
AAAI Workshop on AI, Ethics, and Society, 2016
1632016
State abstractions for lifelong reinforcement learning
D Abel, D Arumugam, L Lehnert, M Littman
International Conference on Machine Learning, 10-19, 2018
1442018
Policy and value transfer in lifelong reinforcement learning
D Abel, Y Jinnai, SY Guo, G Konidaris, M Littman
International Conference on Machine Learning, 20-29, 2018
992018
On the expressivity of Markov reward
D Abel, W Dabney, A Harutyunyan, MK Ho, ML Littman, D Precup, ...
Advances in Neural Information Processing Systems, 2021
862021
People construct simplified mental representations to plan
MK Ho, D Abel, CG Correa, ML Littman, JD Cohen, TL Griffiths
Nature 606 (7912), 129-136, 2022
832022
Agent-agnostic human-in-the-loop reinforcement learning
D Abel, J Salvatier, A Stuhlmüller, O Evans
NeurIPS Workshop on the Future of Interactive Learning Machines, 2016
792016
What can I do here? A theory of affordances in reinforcement learning
K Khetarpal, Z Ahmed, G Comanici, D Abel, D Precup
International Conference on Machine Learning, 2020
672020
Value preserving state-action abstractions
D Abel, N Umbanhowar, K Khetarpal, D Arumugam, D Precup, M Littman
International Conference on Artificial Intelligence and Statistics, 1639-1650, 2020
612020
Goal-based action priors
D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ...
International Conference on Automated Planning and Scheduling, 2015
592015
State abstraction as compression in apprenticeship learning
D Abel, D Arumugam, K Asadi, Y Jinnai, ML Littman, LLS Wong
AAAI Conference on Artificial Intelligence 33, 3134-3142, 2019
572019
Discovering options for exploration by minimizing cover time
Y Jinnai, JW Park, D Abel, G Konidaris
International Conference on Machine Learning, 2019
572019
Exploratory gradient boosting for reinforcement learning in complex domains
D Abel, A Agarwal, F Diaz, A Krishnamurthy, RE Schapire
ICML Workshop on Abstraction in Reinforcement Learning, 2016
512016
The value of abstraction
MK Ho, D Abel, T Griffiths, ML Littman
Current Opinion in Behavioral Sciences, 2019
482019
Finding options that minimize planning time
Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris
International Conference on Machine Learning, 2018
412018
Lipschitz lifelong reinforcement learning
E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman
arXiv preprint arXiv:2001.05411, 2020
362020
A theory of abstraction in reinforcement learning
D Abel
Brown University, 2020
332020
A definition of continual reinforcement learning
D Abel, A Barreto, B Van Roy, D Precup, H van Hasselt, S Singh
Advances in Neural Information Processing Systems, 2023
262023
A theory of state abstraction for reinforcement learning
D Abel
AAAI Conference on Artificial Intelligence 33, 9876-9877, 2019
232019
Settling the reward hypothesis
M Bowling, JD Martin, D Abel, W Dabney
International Conference on Machine Learning, 3003-3020, 2023
222023
The system can't perform the operation now. Try again later.
Articles 1–20