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LEARNING EVALUATION FUNCTIONS FOR GLOBAL OPTIMIZATION
CONTENTS AND ABSTRACT
CHAPTER 1 Introduction
1.1 Motivation: Learning Evaluation Functions
1.2 The Promise of Reinforcement Learning
1.3 Outline of the Dissertation
CHAPTER 2 Learning Evaluation Functions for Sequential Decision Making
2.1 Value Function Approximation (VFA)
2.2 VFA in Deterministic Domains: ``Grow-Support''
2.3 VFA in Acyclic Domains: ``ROUT''
2.4 Discussion
CHAPTER 3 Learning Evaluation Functions for Global Optimization
3.1 Introduction
3.2 The ``STAGE'' Algorithm
3.3 Illustrative Examples
3.4 Theoretical and Computational Issues
CHAPTER 4 STAGE: Empirical Results
4.1 Experimental Methodology
4.2 Bin-packing
4.3 VLSI Channel Routing
4.4 Bayes Network Learning
4.5 Radiotherapy Treatment Planning
4.6 Cartogram Design
4.7 Boolean Satisfiability
4.8 Boggle Board Setup
4.9 Discussion
CHAPTER 5 STAGE: Analysis
5.1 Explaining STAGE's Success
5.2 Empirical Studies of Parameter Choices
5.3 Discussion
CHAPTER 6 STAGE: Extensions
6.1 Least-Squares TD(lambda)
6.2 Transfer
6.3 Discussion
CHAPTER 7 Related Work
7.1 Adaptive Multi-Restart Techniques
7.2 Reinforcement Learning for Optimization
7.3 Rollouts and Learning for AI Search
7.4 Genetic Algorithms
7.5 Discussion
CHAPTER 8 Conclusions
8.1 Contributions
8.2 Future Directions
8.3 Concluding Remarks
APPENDIX A Proofs
A.1 The Best-So-Far Procedure Is Markovian
A.2 Least-Squares TD(1) Is Equivalent to Linear Regression
APPENDIX B Simulated Annealing
B.1 Annealing Schedules
B.2 The ``Modified Lam'' Schedule
B.3 Experiments
APPENDIX C Implementation Details of Problem Instances
C.1 Bin-packing
C.2 VLSI Channel Routing
C.3 Bayes Network Learning
C.4 Radiotherapy Treatment Planning
C.5 Cartogram Design
C.6 Boolean Satisfiability
REFERENCES