Design of Experiments for Reinforcement Learning

This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.Design of Experiments for Reinforcement Learning is written by Christopher Gatti and published by Springer. ISBNs for Design of Experiments for Reinforcement Learning are 9783319121970, 3319121979 and the print ISBNs are 9783319121963, 3319121960.