עקוב אחר
David Abel
David Abel
DeepMind / University of Edinburgh
כתובת אימייל מאומתת בדומיין deepmind.com - דף הבית
כותרת
צוטט על ידי
צוטט על ידי
שנה
Near optimal behavior via approximate state abstraction‏
D Abel, DE Hershkowitz, ML Littman‏
International Conference on Machine Learning, 2915--2923, 2016‏
2042016
Reinforcement learning as a framework for ethical decision making‏
D Abel, J MacGlashan, ML Littman‏
AAAI Workshop on AI, Ethics, and Society, 2016‏
1872016
State abstractions for lifelong reinforcement learning‏
D Abel, D Arumugam, L Lehnert, M Littman‏
International Conference on Machine Learning, 10-19, 2018‏
1632018
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
1302022
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
1102021
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
1062018
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
862016
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
772020
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
752023
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
682020
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
642019
Discovering options for exploration by minimizing cover time‏
Y Jinnai, JW Park, D Abel, G Konidaris‏
International Conference on Machine Learning, 2019‏
642019
The value of abstraction‏
MK Ho, D Abel, T Griffiths, ML Littman‏
Current Opinion in Behavioral Sciences, 2019‏
592019
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
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
522016
Finding options that minimize planning time‏
Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris‏
International Conference on Machine Learning, 2018‏
482018
A theory of abstraction in reinforcement learning‏
D Abel‏
Brown University, 2020‏
462020
Lipschitz lifelong reinforcement learning‏
E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman‏
AAAI Conference on Artificial Intelligence, 2021‏
442021
Settling the reward hypothesis‏
M Bowling, JD Martin, D Abel, W Dabney‏
International Conference on Machine Learning, 3003-3020, 2023‏
422023
The efficiency of human cognition reflects planned information processing‏
MK Ho, D Abel, JD Cohen, ML Littman, TL Griffiths‏
AAAI Conference on Artificial Intelligence, 2020‏
292020
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מאמרים 1–20