Towards autonomous reinforcement learning: Automatic setting of hyper-parameters using Bayesian optimization

With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic setting of hyper-parameter in learning algorithms is a key factor for obtaining good performances regardless of user expertise in the inner workings of the techniques and methodologies. In particular, for a reinforcement learning task, the efficiency of an agent learning a policy in an uncertain environment has a strong dependency on how hyper-parameters in the algorithm are set. In this work, an autonomous framework that employs Bayesian optimization and Gaussian process regression to optimize the hyper-parameters of a reinforcement learning algorithm is proposed. A gridworld example is discussed in order to show how hyper-parameter configurations of a learning algorithm (SARSA) are iteratively improved based on two performance functions.