| Reducing network agnostophobia AR Dhamija, M Günther, T Boult Advances in Neural Information Processing Systems 31, 2018 | 385 | 2018 |
| Hybrid reward architecture for reinforcement learning H Van Seijen, M Fatemi, J Romoff, R Laroche, T Barnes, J Tsang Advances in Neural Information Processing Systems 30, 2017 | 300 | 2017 |
| A theoretical and empirical analysis of expected sarsa H Van Seijen, H Van Hasselt, S Whiteson, M Wiering 2009 ieee symposium on adaptive dynamic programming and reinforcement …, 2009 | 292 | 2009 |
| True online TD (lambda) H Seijen, R Sutton International Conference on Machine Learning, 692-700, 2014 | 133 | 2014 |
| True online temporal-difference learning H Van Seijen, AR Mahmood, PM Pilarski, MC Machado, RS Sutton Journal of Machine Learning Research 17 (145), 1-40, 2016 | 117 | 2016 |
| Systematic generalisation with group invariant predictions F Ahmed, Y Bengio, H Van Seijen, A Courville International Conference on Learning Representations, 2020 | 108 | 2020 |
| A Deeper Look at Planning as Learning from Replay H van Seijen, RS Sutton International Conference on Machine Learning, 2015 | 78 | 2015 |
| Planning by prioritized sweeping with small backups H Van Seijen, R Sutton International Conference on Machine Learning, 361-369, 2013 | 65* | 2013 |
| Hybrid reward architecture for reinforcement learning HH Van Seijen, SMF Booshehri, RMH Laroche, JS Romoff US Patent 10,977,551, 2021 | 47 | 2021 |
| Modular lifelong reinforcement learning via neural composition JA Mendez, H van Seijen, E Eaton arXiv preprint arXiv:2207.00429, 2022 | 46 | 2022 |
| Using a logarithmic mapping to enable lower discount factors in reinforcement learning H Van Seijen, M Fatemi, A Tavakoli Advances in Neural Information Processing Systems 32, 2019 | 35 | 2019 |
| Multi-advisor reinforcement learning R Laroche, M Fatemi, J Romoff, H van Seijen arXiv preprint arXiv:1704.00756, 2017 | 28 | 2017 |
| On value function representation of long horizon problems L Lehnert, R Laroche, H van Seijen Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 27 | 2018 |
| Exploiting Best-Match Equations for Efficient Reinforcement Learning. H van Seijen, S Whiteson, H van Hasselt, M Wiering Journal of Machine Learning Research 12 (6), 2011 | 27 | 2011 |
| Effective multi-step temporal-difference learning for non-linear function approximation H van Seijen arXiv preprint arXiv:1608.05151, 2016 | 23 | 2016 |
| Dead-ends and secure exploration in reinforcement learning M Fatemi, S Sharma, H Van Seijen, SE Kahou International Conference on Machine Learning, 1873-1881, 2019 | 21 | 2019 |
| Efficient abstraction selection in reinforcement learning H van Seijen, S Whiteson, L Kester Computational Intelligence 30 (4), 657-699, 2014 | 20 | 2014 |
| Learning invariances for policy generalization R Tachet, P Bachman, H van Seijen arXiv preprint arXiv:1809.02591, 2018 | 16 | 2018 |
| Separation of concerns in reinforcement learning H van Seijen, M Fatemi, J Romoff, R Laroche arXiv preprint arXiv:1612.05159, 2016 | 15 | 2016 |
| Agent-controller representations: Principled offline rl with rich exogenous information R Islam, M Tomar, A Lamb, Y Efroni, H Zang, A Didolkar, D Misra, X Li, ... arXiv preprint arXiv:2211.00164, 2022 | 14 | 2022 |
Richard S. SuttonKeen, Amii, and University of AlbertaVerified email at richsutton.com
