Improved deep reinforcement learning for robotics through distribution-based experience retention. - Tim de Bruin; Jens Kober; Karl Tuyls; Robert Babuska (IROS, 2016)

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks. - Tim de Bruin; Kim Verbert; Robert Babuska (IEEE Trans. Neural Networks Learn. Syst., 2017)

Reinforcement learning for control: Performance, stability, and deep approximators. - Lucian Busoniu; Tim de Bruin; Domagoj Tolic; Jens Kober; Ivana Palunko (Annu. Rev. Control., 2018)

Experience Selection in Deep Reinforcement Learning for Control. - Tim de Bruin; Jens Kober; Karl Tuyls; Robert Babuska (J. Mach. Learn. Res., 2018)

Integrating State Representation Learning Into Deep Reinforcement Learning. - Tim de Bruin; Jens Kober; Karl Tuyls; Robert Babuska (IEEE Robotics Autom. Lett., 2018)

Vision-based Navigation Using Deep Reinforcement Learning. - Jonas Kulhanek; Erik Derner; Tim de Bruin; Robert Babuska (ECMR, 2019)

Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks. - Tom Bannink; Arash Bakhtiari; Adam Hillier; Lukas Geiger; Tim de Bruin; Leon Overweel; Jelmer Neeven; Koen Helwegen (CoRR, 2020)

Larq Compute Engine: Design, Benchmark and Deploy State-of-the-Art Binarized Neural Networks. - Tom Bannink; Adam Hillier; Lukas Geiger; Tim de Bruin; Leon Overweel; Jelmer Neeven; Koen Helwegen (MLSys, 2021)