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.Saltelli, A., Tarantola, S., Campolongo, F., aamp; Ratto, M. (2004). ... Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. ... Therneau, T., Atkinson, B., aamp; Ripley, B. (2012). rpart: Recursive Partitioning and Regression Trees (Manual for R ... 1067a1078). doi:10.1109/WSC. 2010.5679083 Chapter 5 The Mountain Car Problem The mountain car problem References 93.
|Title||:||Design of Experiments for Reinforcement Learning|
|Publisher||:||Springer - 2014-11-22|