Gatti, Christopher.
Design of Experiments for Reinforcement Learning [electronic resource] / by Christopher Gatti. - XIII, 191 p. 46 illus., 25 illus. in color. online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053 . - Springer Theses, Recognizing Outstanding Ph.D. Research, .
Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
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.
9783319121970
10.1007/978-3-319-12197-0 doi
Engineering.
Logic design.
Artificial intelligence.
Computational intelligence.
Engineering.
Computational Intelligence.
Logic Design.
Artificial Intelligence (incl. Robotics).
Q342
006.3
Design of Experiments for Reinforcement Learning [electronic resource] / by Christopher Gatti. - XIII, 191 p. 46 illus., 25 illus. in color. online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053 . - Springer Theses, Recognizing Outstanding Ph.D. Research, .
Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
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.
9783319121970
10.1007/978-3-319-12197-0 doi
Engineering.
Logic design.
Artificial intelligence.
Computational intelligence.
Engineering.
Computational Intelligence.
Logic Design.
Artificial Intelligence (incl. Robotics).
Q342
006.3