Design of experiments for reinforcement learning
著者
書誌事項
Design of experiments for reinforcement learning
（Springer theses : recognizing outstanding Ph. D. research）
Springer, c2015
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注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
GLOSSARY
ACKNOWLEDGMENT
FOREWARD
1. INTRODUCTION
2. REINFORCEMENT LEARNING
2.1 Applications of reinforcement learning
2.1.1 Benchmark problems
2.1.2 Games
2.1.3 Real-world applications
2.1.4 Generalized domains
2.2 Components of reinforcement learning
2.2.1 Domains
2.2.2 Representations
2.2.3 Learning algorithms
2.3 Heuristics and performance effectors
3. DESIGN OF EXPERIMENTS
3.1 Classical design of experiments
3.2 Contemporary design of experiments
3.3 Design of experiments for empirical algorithm analysis
4. METHODOLOGY
4.1 Sequential CART
4.1.1 CART modeling
4.1.2 Sequential CART modeling
4.1.3 Analysis of sequential CART
4.1.4 Empirical convergence criteria
4.1.5 Example: 2-D 6-hump camelback function
4.2 Kriging metamodeling
4.2.1 Kriging
4.2.2 Deterministic kriging
4.2.3 Stochastic kriging
4.2.4 Covariance function
4.2.5 Implementation
4.2.6 Analysis of kriging metamodels
5. THE MOUNTAIN CAR PROBLEM
5.1 Reinforcement learning implementation
5.2 Sequential CART
5.3 Response surface metamodeling
5.4 Discussion
6. THE TRUCK BACKER-UPPER PROBLEM
6.1 Reinforcement learning implementation
6.2 Sequential CART
6.3 Response surface metamodeling
6.4 Discussion
7. THE TANDEM TRUCK BACKER-UPPER PROBLEM
7.1 Reinforcement learning implementation
7.2 Sequential CART
7.3 Discussion
8. DISCUSSION
8.1 Reinforcement learning
8.2 Experimentation
8.3 Innovations
8.4 Future work
APPENDICES
A. Parameter effects in the game of Chung Toi
B. Design of experiments for the mountain car problem
C. Supporting tables
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