Multi-armed bandit
= Multi-armed bandit
A sequential decision model in which each round selects an action and observes its reward, balancing information gathering against immediate reward. For fixed action means $m(x)$, the <pseudoregret> compares selected actions with an action maximizing $m$. In models with a <link function> and parameter $\theta$, rewards may have means $m_\theta(x)=\mu(\langle x,\theta\rangle)$.