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.
| ISBN: | 9783319385518 |
| Publication date: | 22nd September 2016 |
| Author: | Christopher Gatti |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 191 pages |
| Series: | Springer Theses |
| Genres: |
Artificial intelligence Computer architecture and logic design |
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 features in the following genres: Artificial intelligence, Computer architecture and logic design
Design of Experiments for Reinforcement Learning is available in Paperback
Design of Experiments for Reinforcement Learning was written by Christopher Gatti and published by Springer an imprint of Springer International Publishing
Design of Experiments for Reinforcement Learning has 191 pages
Yes it is part of Springer Theses series