| Dive into deep learning A Zhang, ZC Lipton, M Li, AJ Smola arXiv preprint arXiv:2106.11342, 2021 | 1095 | 2021 |
| Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning JD Williams, K Asadi, G Zweig arXiv preprint arXiv:1702.03274, 2017 | 406 | 2017 |
| An Alternative Softmax Operator for Reinforcement Learning K Asadi, ML Littman Proceedings of the 34th International Conference on Machine Learning, 243-252, 2017 | 224 | 2017 |
| Lipschitz Continuity in Model-based Reinforcement Learning K Asadi, D Misra, ML Littman Proceedings of the 35th International Conference on Machine Learning, 2018 | 168 | 2018 |
| Deepmellow: removing the need for a target network in deep Q-learning S Kim, K Asadi, M Littman, G Konidaris Proceedings of the Twenty Eighth International Joint Conference on …, 2019 | 76* | 2019 |
| State abstraction as compression in apprenticeship learning D Abel, D Arumugam, K Asadi, Y Jinnai, ML Littman, LLS Wong Proceedings of the AAAI Conference on Artificial Intelligence 33, 3134-3142, 2019 | 59 | 2019 |
| Combating the Compounding-Error Problem with a Multi-step Model K Asadi, D Misra, S Kim, ML Littman arXiv preprint arXiv:1905.13320, 2019 | 57 | 2019 |
| Lipschitz lifelong reinforcement learning E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8270-8278, 2021 | 38 | 2021 |
| Mean Actor Critic K Asadi, C Allen, M Roderick, A Mohamed, G Konidaris, M Littman arXiv preprint arXiv:1709.00503, 2017 | 36* | 2017 |
| Continuous doubly constrained batch reinforcement learning R Fakoor, J Mueller, K Asadi, P Chaudhari, AJ Smola arXiv preprint arXiv:2102.09225, 2021 | 30 | 2021 |
| Deep radial-basis value functions for continuous control K Asadi, N Parikh, RE Parr, GD Konidaris, ML Littman Proceedings of the AAAI Conference on Artificial Intelligence, 2021 | 27* | 2021 |
| Sample-efficient Reinforcement Learning for Dialog Control K Asadi, JD Williams arXiv preprint arXiv:1612.06000, 2016 | 25 | 2016 |
| Strengths, weaknesses, and combinations of model-based and model-free reinforcement learning K Asadi Department of Computing Science University of Alberta, 2015 | 14 | 2015 |
| 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 | 13 | 2018 |
| Towards a Simple Approach to Multi-step Model-based Reinforcement Learning K Asadi, E Cater, D Misra, ML Littman arXiv preprint arXiv:1811.00128, 2018 | 13 | 2018 |
| Equivalence between wasserstein and value-aware model-based reinforcement learning K Asadi, E Cater, D Misra, ML Littman FAIM Workshop on Prediction and Generative Modeling in Reinforcement Learning 3, 2018 | 13* | 2018 |
| Resetting the optimizer in deep RL: An empirical study K Asadi, R Fakoor, S Sabach Advances in Neural Information Processing Systems 36, 2023 | 9 | 2023 |
| Fair E3: Efficient welfare-centric fair reinforcement learning C Cousins, K Asadi, ML Littman 5th Multidisciplinary Conference on Reinforcement Learning and Decision …, 2022 | 6 | 2022 |
| Learning State Abstractions for Transfer in Continuous Control K Asadi, D Abel, ML Littman arXiv preprint arXiv:2002.05518, 2020 | 6 | 2020 |
| TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models Z Liu, J Zhang, K Asadi, Y Liu, D Zhao, S Sabach, R Fakoor arXiv preprint arXiv:2310.05905, 2023 | 5 | 2023 |
Michael LittmanBrown University在 brown.edu 的电子邮件经过验证
