Temperature handler in radiosusing machine learning : Temperature handler in radiosusing machine learning

Abstract: Machine learning is revolutionising the field of automation in various industries. But there exist powerful methods and tools in a number of cases that do not include the learning process like machine learning does. In this thesis, controllers for compensating for overheating in radio stations are built, evaluated and compared. The controllers are based on two different approaches: the first approach is based on model predictive control (MPC), and the second one is based on methods of reinforcement learning (RL). This report compares those two approaches, and reports qualitative and quantitative differences.