The $\alpha$-UCB Bandit for stochastic regret minimization described in [1] .
module P : AlphaUCBParamtype bandit = banditEstimatesThe internal data structure of the bandit algorithm.
val initialBandit : banditThe internal data structure of the bandit algorithm.
The initial state of the bandit algorithm.
The initial state of the bandit algorithm.
step r advances the bandit game one step, where r is the reward for the last action. The result of this call is the next action, encoded as an integer in $ \{ 0, \cdots , K-1 \} $, and the new state of the bandit. The reward range depends on the bandit algorithm in use and the first reward provided to the algorithm is discarded.