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• Markov Decision Processes Value Prediction Problems Control For Further Exploration.
• (source: Nielsen Book Data)

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
(source: Nielsen Book Data)

• Markov Decision Processes Value Prediction Problems Control For Further Exploration.
• (source: Nielsen Book Data)

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Foundations of probability
• 3. Stochastic processes and Markov chains
• 4. Finite-armed stochastic bandits
• 5. Concentration of measure
• 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
• 11. The Exp3 algorithm
• 12. The Exp3-IX algorithm
• 13. Lower bounds: basic ideas
• 14. Foundations of information theory
• 15. Minimax lower bounds
• 16. Asymptotic and instance dependent lower bounds
• 17. High probability lower bounds
• 18. Contextual bandits
• 19. Stochastic linear bandits
• 20. Confidence bounds for least squares estimators
• 21. Optimal design for least squares estimators
• 22. Stochastic linear bandits with finitely many arms
• 23. Stochastic linear bandits with sparsity
• 24. Minimax lower bounds for stochastic linear bandits
• 25. Asymptotic lower bounds for stochastic linear bandits
• 26. Foundations of convex analysis
• 27. Exp3 for adversarial linear bandits
• 28. Follow the regularized leader and mirror descent
• 29. The relation between adversarial and stochastic linear bandits
• 30. Combinatorial bandits
• 31. Non-stationary bandits
• 32. Ranking
• 33. Pure exploration
• 34. Foundations of Bayesian learning
• 35. Bayesian bandits
• 36. Thompson sampling
• 37. Partial monitoring
• 38. Markov decision processes.
• (source: Nielsen Book Data)

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
(source: Nielsen Book Data)