David Abel
David Abel
Research Scientist, DeepMind
Bestätigte E-Mail-Adresse bei google.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Near optimal behavior via approximate state abstraction
D Abel, DE Hershkowitz, ML Littman
International Conference on Machine Learning, 2915--2923, 2016
682016
Reinforcement learning as a framework for ethical decision making
D Abel, J MacGlashan, ML Littman
AAAI Workshop on AI, Ethics, and Society, 2016
642016
State abstractions for lifelong reinforcement learning
D Abel, D Arumugam, L Lehnert, M Littman
International Conference on Machine Learning, 10-19, 2018
382018
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
372016
Goal-based action priors
D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ...
International Conference on Automated Planning and Scheduling, 2015
352015
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
322016
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
192018
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
182019
The value of abstraction
MK Ho, D Abel, T Griffiths, ML Littman
Current Opinion in Behavioral Sciences, 2019
92019
Discovering options for exploration by minimizing cover time
Y Jinnai, JW Park, D Abel, G Konidaris
International Conference on Machine Learning, 2019
72019
Finding options that minimize planning time
Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris
International Conference on Machine Learning, 2018
72018
Toward good abstractions for lifelong learning
D Abel, D Arumugam, L Lehnert, ML Littman
NeurIPS Workshop on Hierarchical Reinforcement Learning, 2017
72017
Affordances as transferable knowledge for planning agents
G Barth-Maron, D Abel, J MacGlashan, S Tellex
AAAI Fall Symposium Series, 2014
72014
Toward affordance-aware planning
D Abel, G Barth-Maron, J MacGlashan, S Tellex
RSS Workshop on Affordances: Affordances in Vision for Cognitive Robotics, 2014
7*2014
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
62020
Modeling latent attention within neural networks
C Grimm, D Arumugam, S Karamcheti, D Abel, LLS Wong, ML Littman
arXiv preprint arXiv:1706.00536, 2017
6*2017
A theory of state abstraction for reinforcement learning
D Abel
AAAI Conference on Artificial Intelligence 33, 9876-9877, 2019
52019
Bandit-based solar panel control
D Abel, EC Williams, S Brawner, E Reif, ML Littman
Innovative Applications of Artificial Intelligence, 2018
52018
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
42020
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
42020
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20