text: abstract
  we propose the kl-ucb++ algorithm for regret minimization in stochastic bandit models with
  exponential families of distributions. we prove that it is simultaneously asymptotically optimal
  (in the sense of lai and robbins’ lower bound) and minimax optimal. this is the first algorithm
  proved to enjoy these two properties at the same time. this work thus merges two different lines of
  research with simple and clear proofs.
  keywords: stochastic multi-armed bandits, regret analysis, upper confidence bound (ucb), minimax optimality, asymptotic optimality.