| An emphatic approach to the problem of off-policy temporal-difference learning RS Sutton, AR Mahmood, M White (JMLR) Journal of Machine Learning Research 17, 2016 | 226 | 2016 |
| Weighted importance sampling for off-policy learning with linear function approximation AR Mahmood, H van Hasselt, RS Sutton (NeurIPS) Advances in Neural Information Processing Systems 27, 2014 | 135 | 2014 |
| Benchmarking reinforcement learning algorithms on real-world robots AR Mahmood, D Korenkevych, G Vasan, W Ma, J Bergstra (CoRL) Proceedings of the 2nd Annual Conference on Robot Learning, 2018 | 133 | 2018 |
| True online temporal-difference learning H van Seijen, AR Mahmood, PM Pilarski, MC Machado, RS Sutton (JMLR) Journal of Machine Learning Research 17, 2016 | 102 | 2016 |
| Tuning-free step-size adaptation AR Mahmood, RS Sutton, T Degris, PM Pilarski (ICASSP) Acoustics, Speech and Signal Processing, 2012 IEEE International …, 2012 | 59 | 2012 |
| Setting up a reinforcement learning task with a real-world robot AR Mahmood, D Korenkevych, BJ Komer, J Bergstra (IROS) 2018 IEEE/RSJ International Conference on Intelligent Robots and …, 2018 | 54 | 2018 |
| Multi-step off-policy learning without importance sampling ratios AR Mahmood, H Yu, RS Sutton arXiv preprint arXiv:1702.03006, 2017 | 45 | 2017 |
| Off-policy TD (λ) with a true online equivalence H van Hasselt, AR Mahmood, RS Sutton (UAI) Proceedings of the 30th Conference on Uncertainty in Artificial …, 2014 | 39 | 2014 |
| A new Q (λ) with interim forward view and Monte Carlo equivalence RS Sutton, AR Mahmood, D Precup, M CA, H van Hasselt, U CA (ICML) In International Conference on Machine Learning, 2014 | 38 | 2014 |
| Representation Search through Generate and Test AR Mahmood, RS Sutton Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013 | 38 | 2013 |
| On generalized Bellman equations and temporal-difference learning H Yu, AR Mahmood, RS Sutton (JMLR) The Journal of Machine Learning Research 19 (1), 1864-1912, 2018 | 33 | 2018 |
| Emphatic temporal-difference learning AR Mahmood, H Yu, M White, RS Sutton In European Workshops on Reinforcement Learning, 2015 | 31 | 2015 |
| Off-policy learning based on weighted importance sampling with linear computational complexity AR Mahmood, RS Sutton (UAI) Proceedings of the 31st Conference on Uncertainty in Artificial …, 2015 | 27 | 2015 |
| Autoregressive policies for continuous control deep reinforcement learning D Korenkevych, AR Mahmood, G Vasan, J Bergstra (IJCAI) Proceedings of the 28th International Joint Conference on Artificial …, 2019 | 23 | 2019 |
| Incremental Off-policy Reinforcement Learning Algorithms A Mahmood University of Alberta, 2017 | 15 | 2017 |
| Continual backprop: stochastic gradient descent with persistent randomness S Dohare, RS Sutton, AR Mahmood arXiv preprint arXiv:2108.06325, 2021 | 11 | 2021 |
| Structure Learning of Causal Bayesian Networks: A Survey A Mahmood Department of Computing Science, University of Alberta, Edmonton, Canada …, 2011 | 9 | 2011 |
| Automatic Step-size Adaptation In Incremental Supervised Learning A Mahmood University of Alberta, 2010 | 9 | 2010 |
| Greedification operators for policy optimization: investigating forward and reverse KL divergences A Chan, H Silva, S Lim, T Kozuno, AR Mahmood, M White (JMLR) Journal of Machine Learning Research, 2022 | 6 | 2022 |
| Real-time real-world reinforcement learning systems and methods AR Mahmood, BJ Komer, D Korenkevych US Patent App. 16/560,761, 2020 | 6 | 2020 |
Richard S. SuttonDeepMind, Amii, and University of AlbertaBestätigte E-Mail-Adresse bei richsutton.com
