| Reinforcement learning for control: Performance, stability, and deep approximators L Buşoniu, T De Bruin, D Tolić, J Kober, I Palunko Annual Reviews in Control 46, 8-28, 2018 | 498 | 2018 |
| Railway track circuit fault diagnosis using recurrent neural networks T De Bruin, K Verbert, R Babuška IEEE transactions on neural networks and learning systems 28 (3), 523-533, 2016 | 365 | 2016 |
| Integrating State Representation Learning into Deep Reinforcement Learning T de Bruin, J Kober, K Tuyls, R Babuska IEEE Robotics and Automation Letters 3 (3), 1394-1401, 2018 | 146 | 2018 |
| The importance of experience replay database composition in deep reinforcement learning T De Bruin, J Kober, K Tuyls, R Babuška Deep reinforcement learning workshop, NIPS, 2015 | 129 | 2015 |
| Vision-based navigation using deep reinforcement learning J Kulhánek, E Derner, T De Bruin, R Babuška 2019 european conference on mobile robots (ECMR), 1-8, 2019 | 93 | 2019 |
| Experience selection in deep reinforcement learning for control T de Bruin, J Kober, K Tuyls, R Babuška The Journal of Machine Learning Research 19 (1), 347-402, 2018 | 85 | 2018 |
| Improved Deep Reinforcement Learning for Robotics Through Distribution-based Experience Retention T de Bruin, J Kober, K Tuyls, R Babuška IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016 | 52 | 2016 |
| Larq compute engine: Design, benchmark and deploy state-of-the-art binarized neural networks T Bannink, A Hillier, L Geiger, T de Bruin, L Overweel, J Neeven, ... Proceedings of Machine Learning and Systems 3, 680-695, 2021 | 51 | 2021 |
| Fine-tuning deep RL with gradient-free optimization T de Bruin, J Kober, K Tuyls, R Babuška IFAC-PapersOnLine 53 (2), 8049-8056, 2020 | 10 | 2020 |
| Off-policy experience retention for deep actor-critic learning T de Bruin, J Kober, K Tuyls, R Babuška Deep Reinforcement Learning Workshop, Advances in Neural Information …, 2016 | 10 | 2016 |
| Artificial neural networks D Learning 2023 International, 2019 | 3 | 2019 |
| Sample effficient deep reinforcement learning for control T de Bruin | 1 | 2020 |
| Neural network based condition monitoring for track circuits T de Bruin Msc. Thesis, Delft University of Technology, 2015 | 1 | 2015 |
| Fine-tuning Deep RL with Gradient-Free Optimization TBJK Karl, TR Babuška, T de Bruin, J Kober, K Tuyls, R Babuška | | 2020 |
Robert BabuskaProfessor, Delft University of Technology, Czech Technical University PragueВерификована је имејл адреса на tudelft.nl