Book contents
- Frontmatter
- Contents
- Preface
- Notation
- Part I Bandits, Probability and Concentration
- Part II Stochastic Bandits with Finitely Many Arms
- 6 The Explore-Then-Commit Algorithm
- 7 The Upper Confidence Bound Algorithm
- 8 The Upper Confidence Bound Algorithm: Asymptotic Optimality
- 9 The Upper Confidence Bound Algorithm: Minimax Optimality
- 10 The Upper Confidence Bound Algorithm: Bernoulli Noise
- Part III Adversarial Bandits with Finitely Many Arms
- Part IV Lower Bounds for Bandits with Finitely Many Arms
- Part V Contextual and Linear Bandits
- Part VI Adversarial Linear Bandits
- Part VII Other Topics
- Part VIII Beyond Bandits
- Bibliography
- Index
8 - The Upper Confidence Bound Algorithm: Asymptotic Optimality
from Part II - Stochastic Bandits with Finitely Many Arms
Published online by Cambridge University Press: 04 July 2020
Book contents
- Frontmatter
- Contents
- Preface
- Notation
- Part I Bandits, Probability and Concentration
- Part II Stochastic Bandits with Finitely Many Arms
- 6 The Explore-Then-Commit Algorithm
- 7 The Upper Confidence Bound Algorithm
- 8 The Upper Confidence Bound Algorithm: Asymptotic Optimality
- 9 The Upper Confidence Bound Algorithm: Minimax Optimality
- 10 The Upper Confidence Bound Algorithm: Bernoulli Noise
- Part III Adversarial Bandits with Finitely Many Arms
- Part IV Lower Bounds for Bandits with Finitely Many Arms
- Part V Contextual and Linear Bandits
- Part VI Adversarial Linear Bandits
- Part VII Other Topics
- Part VIII Beyond Bandits
- Bibliography
- Index
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- Bandit Algorithms , pp. 97 - 102Publisher: Cambridge University PressPrint publication year: 2020