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Tim de Bruin
Tim de Bruin
Plumerai
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Title
Cited by
Cited by
Year
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
2252016
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
1932018
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
922018
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
902015
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
492018
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
442019
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
322016
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
102021
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
Fine-tuning deep RL with gradient-free optimization
T de Bruin, J Kober, K Tuyls, R Babuška
IFAC-PapersOnLine 53 (2), 8049-8056, 2020
42020
Sample effficient deep reinforcement learning for control
T de Bruin
2020
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