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Design of Experiments for Reinforcement Learning
Christopher Gatti
€ 126.77
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Description for Design of Experiments for Reinforcement Learning
Paperback. Series: Springer Theses. Num Pages: 204 pages, 21 black & white illustrations, 25 colour illustrations, 31 black & white tables, biograp. BIC Classification: UYF; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 11. Weight in Grams: 326.
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.
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.
Product Details
Format
Paperback
Publication date
2016
Publisher
Springer International Publishing AG Switzerland
Number of pages
204
Condition
New
Series
Springer Theses
Number of Pages
191
Place of Publication
Cham, Switzerland
ISBN
9783319385518
SKU
V9783319385518
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Christopher Gatti
Christopher Gatti received his PhD in Decision Sciences and Engineering Systems from Rensselaer Polytechnic Institute (RPI). During his time at RPI, his work focused on machine learning and statistics, with applications in reinforcement learning, graph search, stem cell RNA analysis, and neuro-electrophysiological signal analysis. Prior to beginning his graduate work at RPI, he received a BSE in mechanical engineering and ... Read more
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