Algorithms for the continuous setting of this problem include OLOP and HOO.
Algorithms for the continuous setting of this problem include OLOP and HOO. Garivier and Cappe used Kullback-Leibler divergence to create an optimal, non-parametric algorithm for this problem. Exponential weighting is used in the Exp3 algorithm for this problem's adversarial setting. Lai and Robbins proved that no algorithm can have better than log(n) performance for this problem. An algorithm for this problem is used as a subroutine in UCT Monte Carlo tree search. Auer and Cesa-Bianchi designed an algorithm for this problem's stochastic setting that tracks upper confidence bounds on a set of sampled random variables. This is the simplest problem in which algorithms must make a tradeoff (*) between exploration and exploitation, with the goal of minimizing regret. In this online machine learning problem, an agent repeatedly chooses from a set of actions, each of which generates a noisy reward. For 10 points, name this problem which is named for its resemblance to slot machines.
Algorithms for the continuous setting of this problem include OLOP and HOO.
multi-armed bandit problem [accept more specific answers that include "continuous", "adversarial", or "stochastic"]