Buch, Englisch, 150 Seiten, PB, Format (B × H): 148 mm x 210 mm, Gewicht: 222 g
Reihe: Berichte aus der Informatik
Buch, Englisch, 150 Seiten, PB, Format (B × H): 148 mm x 210 mm, Gewicht: 222 g
Reihe: Berichte aus der Informatik
ISBN: 978-3-8440-3448-6
Verlag: Shaker
Programming desired task solutions for modern complex systems is often challenging, since it relies on detailed system understanding. In such cases, learning from data can be a useful alternative. Reinforcement learning (RL) is a general approach to learn policies while interacting with the system. In this thesis, we investigate the use of RL for several industrial applications, such as control of a robot arm and a throttle valve, and propose RL approaches while addressing practical constraints.
