5 Exploring the search space with bandit-style policies
This chapter covers
- The multi-armed bandit problem and how it’s related to BayesOpt
- The Upper Confidence Bound policy in BayesOpt
- The Thompson sampling policy in BayesOpt
Which slot machine should you play at a casino to maximize your winnings? How can you develop a strategy to intelligently try out multiple slot machines and narrow down the most profitable machine? What does this problem have to do with BayesOpt? These are the questions this chapter will help us answer.
Chapter 4 was our introduction to BayesOpt policies, which decide how the search space should be explored and inspected. The exploration strategy of a BayesOpt policy should guide us toward the optimum of the objective ...
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