Stochastic control approach to the multi-armed bandit problems

Thesis

A multi-armed bandit is the simplest problem to study learning under uncertainty when decisions affect information. A standard approach to the multi-armed bandit often gives a heuristic construction of an algorithm and proves its regret bound. Following a constructive approach, it is often possible to find a scenario where following heuristic approaches gives a poor decision.

In this thesis, we consider solving the multi-armed bandit problem from first principles, in terms of stoch...

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Authors: Treetanthiploet, T

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: optimal learning; multi-armed bandit; stochastic control; Gittins index
• Subjects: Machine learning; Mathematics