Hyperparameter optimisation using Q-learning based algorithms

Abstract: Machine learning algorithms have many applications, both for academic and industrial purposes. Examples of applications are classification of diffraction patterns in materials science and classification of properties in chemical compounds within the pharmaceutical industry. For these algorithms to be successful they need to be optimised,  part of this is achieved by training the algorithm, but there are components of the algorithms that cannot be trained. These hyperparameters have to be tuned separately. The focus of this work was optimisation of hyperparameters in classification algorithms based on convolutional neural networks. The purpose of this thesis was to investigate the possibility of using reinforcement learning algorithms, primarily Q-learning, as the optimising algorithm.  Three different algorithms were investigated, Q-learning, double Q-learning and a Q-learning inspired algorithm, which was designed during this work. The algorithms were evaluated on different problems and compared to a random search algorithm, which is one of the most common optimisation tools for this type of problem. All three algorithms were capable of some learning, however the Q-learning inspired algorithm was the only one to outperform the random search algorithm on the test problems.  Further, an iterative scheme of the Q-learning inspired algorithm was implemented, where the algorithm was allowed to refine the search space available to it. This showed further improvements of the algorithms performance and the results indicate that similar performance to the random search may be achieved in a shorter period of time, sometimes reducing the computational time by up to 40%.