David Abel
David Abel
Research Scientist, DeepMind
Dirección de correo verificada de - Página principal
Citado por
Citado por
Near optimal behavior via approximate state abstraction
D Abel, DE Hershkowitz, ML Littman
International Conference on Machine Learning, 2915--2923, 2016
Reinforcement learning as a framework for ethical decision making
D Abel, J MacGlashan, ML Littman
AAAI Workshop on AI, Ethics, and Society, 2016
State abstractions for lifelong reinforcement learning
D Abel, D Arumugam, L Lehnert, M Littman
International Conference on Machine Learning, 10-19, 2018
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
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
Goal-based action priors
D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ...
International Conference on Automated Planning and Scheduling, 2015
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
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
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
Discovering options for exploration by minimizing cover time
Y Jinnai, JW Park, D Abel, G Konidaris
International Conference on Machine Learning, 2019
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
The value of abstraction
MK Ho, D Abel, T Griffiths, ML Littman
Current Opinion in Behavioral Sciences, 2019
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
Finding options that minimize planning time
Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris
International Conference on Machine Learning, 2018
Toward affordance-aware planning
D Abel, G Barth-Maron, J MacGlashan, S Tellex
RSS Workshop on Affordances: Affordances in Vision for Cognitive Robotics, 2014
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
Lipschitz lifelong reinforcement learning
E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman
arXiv preprint arXiv:2001.05411, 2020
Modeling latent attention within neural networks
C Grimm, D Arumugam, S Karamcheti, D Abel, LLS Wong, ML Littman
arXiv preprint arXiv:1706.00536, 2017
Revisiting Peng's Q () for modern reinforcement learning
T Kozuno, Y Tang, M Rowland, R Munos, S Kapturowski, W Dabney, ...
arXiv preprint arXiv:2103.00107, 2021
Concepts in bounded rationality: Perspectives from reinforcement learning
D Abel
Brown University, 2019
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20