David Abel
David Abel
Ph.D Candidate, Brown University
Verified email at brown.edu - Homepage
Title
Cited by
Cited by
Year
Reinforcement learning as a framework for ethical decision making
D Abel, J MacGlashan, ML Littman
AAAI Workshop on AI, Ethics, and Society, 2016
582016
Near optimal behavior via approximate state abstraction
D Abel, DE Hershkowitz, ML Littman
International Conference on Machine Learning, 2915--2923, 2016
582016
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
352016
Goal-based action priors
D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ...
International Conference on Automated Planning and Scheduling, 2015
332015
State abstractions for lifelong reinforcement learning
D Abel, D Arumugam, L Lehnert, M Littman
International Conference on Machine Learning, 10-19, 2018
292018
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
272016
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
142019
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
142018
Discovering options for exploration by minimizing cover time
Y Jinnai, JW Park, D Abel, G Konidaris
International Conference on Machine Learning, 2019
72019
Affordances as transferable knowledge for planning agents
G Barth-Maron, D Abel, J MacGlashan, S Tellex
AAAI Fall Symposium Series, 2014
72014
The value of abstraction
MK Ho, D Abel, T Griffiths, ML Littman
Current Opinion in Behavioral Sciences, 2019
62019
Finding options that minimize planning time
Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris
International Conference on Machine Learning, 2018
62018
Toward affordance-aware planning
D Abel, G Barth-Maron, J MacGlashan, S Tellex
RSS Workshop on Affordances: Affordances in Vision for Cognitive Robotics, 2014
6*2014
Modeling latent attention within neural networks
C Grimm, D Arumugam, S Karamcheti, D Abel, LLS Wong, ML Littman
arXiv preprint arXiv:1706.00536, 2017
5*2017
Toward good abstractions for lifelong learning
D Abel, D Arumugam, L Lehnert, ML Littman
NeurIPS Workshop on Hierarchical Reinforcement Learning, 2017
52017
Mitigating planner overfitting in model-based reinforcement learning
D Arumugam, D Abel, K Asadi, N Gopalan, C Grimm, JK Lee, L Lehnert, ...
arXiv preprint arXiv:1812.01129, 2018
42018
Bandit-based solar panel control
D Abel, EC Williams, S Brawner, E Reif, ML Littman
Innovative Applications of Artificial Intelligence, 2018
42018
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
32020
A theory of state abstraction for reinforcement learning
D Abel
AAAI Conference on Artificial Intelligence 33, 9876-9877, 2019
32019
simple_rl: Reproducible reinforcement learning in python
D Abel
ICLR Workshop on Reproducibility in Machine Learning, 2019
22019
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