| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 | 5140 | 2014 |
| Vector-based navigation using grid-like representations in artificial agents A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ... Nature 557 (7705), 429-433, 2018 | 720 | 2018 |
| Deep reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ... arXiv preprint arXiv:1512.07679, 2015 | 719 | 2015 |
| Off-policy actor-critic T Degris, M White, RS Sutton arXiv preprint arXiv:1205.4839, 2012 | 639 | 2012 |
| Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction RS Sutton, J Modayil, M Delp, T Degris, PM Pilarski, A White, D Precup The 10th International Conference on Autonomous Agents and Multiagent …, 2011 | 604 | 2011 |
| Model-free reinforcement learning with continuous action in practice T Degris, PM Pilarski, RS Sutton 2012 American control conference (ACC), 2177-2182, 2012 | 339 | 2012 |
| The predictron: End-to-end learning and planning D Silver, H Hasselt, M Hessel, T Schaul, A Guez, T Harley, ... International Conference on Machine Learning, 3191-3199, 2017 | 305 | 2017 |
| Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning PM Pilarski, MR Dawson, T Degris, F Fahimi, JP Carey, RS Sutton 2011 IEEE international conference on rehabilitation robotics, 1-7, 2011 | 203 | 2011 |
| Learning the structure of factored markov decision processes in reinforcement learning problems T Degris, O Sigaud, PH Wuillemin Proceedings of the 23rd international conference on Machine learning, 257-264, 2006 | 159 | 2006 |
| Adaptive artificial limbs: a real-time approach to prediction and anticipation PM Pilarski, MR Dawson, T Degris, JP Carey, KM Chan, JS Hebert, ... IEEE Robotics & Automation Magazine 20 (1), 53-64, 2013 | 86 | 2013 |
| Tuning-free step-size adaptation AR Mahmood, RS Sutton, T Degris, PM Pilarski 2012 IEEE international conference on acoustics, speech and signal …, 2012 | 84 | 2012 |
| Dynamic switching and real-time machine learning for improved human control of assistive biomedical robots PM Pilarski, MR Dawson, T Degris, JP Carey, RS Sutton 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and …, 2012 | 63 | 2012 |
| Adapting behavior via intrinsic reward: A survey and empirical study C Linke, NM Ady, M White, T Degris, A White Journal of artificial intelligence research 69, 1287-1332, 2020 | 51 | 2020 |
| A spiking neuron model of head-direction cells for robot orientation T Degris, L Lachèze, C Boucheny, A Arleo | 31 | 2004 |
| Factored markov decision processes T Degris, O Sigaud Markov Decision Processes in Artificial Intelligence, 99-126, 2013 | 27 | 2013 |
| Meta-descent for online, continual prediction A Jacobsen, M Schlegel, C Linke, T Degris, A White, M White Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3943-3950, 2019 | 22 | 2019 |
| Rapid response of head direction cells to reorienting visual cues: a computational model T Degris, O Sigaud, SI Wiener, A Arleo Neurocomputing 58, 675-682, 2004 | 22 | 2004 |
| Chi-square tests driven method for learning the structure of factored mdps T Degris, O Sigaud, PH Wuillemin arXiv preprint arXiv:1206.6842, 2012 | 21 | 2012 |
| Scaling-up knowledge for a cognizant robot T Degris, J Modayil AAAI Spring Symposium on Designing Intelligent Robots: Reintegrating AI., 2012 | 16 | 2012 |
| Apprentissage par renforcement dans les processus de décision markoviens factorisés T Degris Paris 6, 2007 | 11 | 2007 |
