Tim de Bruin
Tim de Bruin
Plumerai
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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
1592016
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
1042018
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
702015
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
642018
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
342018
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
272016
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
242019
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
92016
Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks
T Bannink, A Bakhtiari, A Hillier, L Geiger, T de Bruin, L Overweel, ...
arXiv preprint arXiv:2011.09398, 2020
12020
Fine-tuning Deep RL with Gradient-Free Optimization⋆
T de Bruin, J Kober, K Tuyls, R Babuška
Proceedings of the IFAC World Congress, 2020
12020
Sample effficient deep reinforcement learning for control
T de Bruin
2020
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Artículos 1–11