Near optimal behavior via approximate state abstraction D Abel, DE Hershkowitz, ML Littman International Conference on Machine Learning, 2915--2923, 2016 | 187 | 2016 |
Reinforcement learning as a framework for ethical decision making D Abel, J MacGlashan, ML Littman AAAI Workshop on AI, Ethics, and Society, 2016 | 162 | 2016 |
State abstractions for lifelong reinforcement learning D Abel, D Arumugam, L Lehnert, M Littman International Conference on Machine Learning, 10-19, 2018 | 144 | 2018 |
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 | 99 | 2018 |
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 | 86 | 2021 |
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 | 83 | 2022 |
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 | 79 | 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 | 67 | 2020 |
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 | 61 | 2020 |
Goal-based action priors D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ... International Conference on Automated Planning and Scheduling, 2015 | 59 | 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 | 57 | 2019 |
Discovering options for exploration by minimizing cover time Y Jinnai, JW Park, D Abel, G Konidaris International Conference on Machine Learning, 2019 | 57 | 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 | 51 | 2016 |
The value of abstraction MK Ho, D Abel, T Griffiths, ML Littman Current Opinion in Behavioral Sciences, 2019 | 48 | 2019 |
Finding options that minimize planning time Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris International Conference on Machine Learning, 2018 | 41 | 2018 |
Lipschitz lifelong reinforcement learning E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman arXiv preprint arXiv:2001.05411, 2020 | 36 | 2020 |
A theory of abstraction in reinforcement learning D Abel Brown University, 2020 | 33 | 2020 |
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 | 25 | 2023 |
A theory of state abstraction for reinforcement learning D Abel AAAI Conference on Artificial Intelligence 33, 9876-9877, 2019 | 23 | 2019 |
Settling the reward hypothesis M Bowling, JD Martin, D Abel, W Dabney International Conference on Machine Learning, 3003-3020, 2023 | 22 | 2023 |