Design of Experiments for Reinforcement Learning

Beschreibung

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.

Nominated by the Rensselaer Polytechnic Institute as an outstanding Ph.D. thesis Explains reinforcement learning through a range of problems by exploring what affects reinforcement learning and what contributes to a successful implementation Includes a contemporary design of experiments methods, comprising of a novel sequential experimentation procedure that finds convergent learning algorithm parameter subregions and stochastic kriging for response surface metamodeling Includes supplementary material: sn.pub/extras

Autor*in

Christopher Gatti

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Kriging Covariance Functions Reinforcement Learning Algorithm Response Surface Metamodeling Sequential CART Stochastic Kriging

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