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Published online by Cambridge University Press: 20 July 2023
In the standard multiarmed bandit problem, one observes a fixed number of arms. To achieve optimal regret bounds, one estimates confidence intervals of the arms by counting. In the contextual bandit problem, one observes side information for each arm, which can be used as features for more accurate confidence interval estimation. This chapter studies contextual bandit problems with both linear and nonlinear models
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