| Near optimal behavior via approximate state abstraction D Abel, DE Hershkowitz, ML Littman International Conference on Machine Learning, 2915--2923, 2016 | 206 | 2016 |
| Reinforcement learning as a framework for ethical decision making D Abel, J MacGlashan, ML Littman AAAI Workshop on AI, Ethics, and Society, 2016 | 184 | 2016 |
| State abstractions for lifelong reinforcement learning D Abel, D Arumugam, L Lehnert, M Littman International Conference on Machine Learning, 10-19, 2018 | 165 | 2018 |
| 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 | 127 | 2022 |
| 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 | 107 | 2021 |
| 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 | 107 | 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 | 86 | 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 | 78 | 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 | 72 | 2023 |
| 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 | 66 | 2020 |
| 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 | 63 | 2019 |
| Discovering options for exploration by minimizing cover time Y Jinnai, JW Park, D Abel, G Konidaris International Conference on Machine Learning, 2019 | 62 | 2019 |
| 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 |
| The value of abstraction MK Ho, D Abel, T Griffiths, ML Littman Current Opinion in Behavioral Sciences, 2019 | 58 | 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 | 52 | 2016 |
| Finding options that minimize planning time Y Jinnai, D Abel, DE Hershkowitz, M Littman, G Konidaris International Conference on Machine Learning, 2018 | 49 | 2018 |
| A theory of abstraction in reinforcement learning D Abel Brown University, 2020 | 47 | 2020 |
| Lipschitz lifelong reinforcement learning E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman AAAI Conference on Artificial Intelligence, 2021 | 44 | 2021 |
| Settling the reward hypothesis M Bowling, JD Martin, D Abel, W Dabney International Conference on Machine Learning, 3003-3020, 2023 | 40 | 2023 |
| 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 | 29 | 2020 |
Michael LittmanBrown UniversityVerified email at brown.edu