Sequential Monte Carlo Bandits

arXiv:1808.02933v4 Announce Type: replace-cross
Abstract: We extend Bayesian multi-armed bandit (MAB) algorithms beyond their original setting by making use of sequential Monte Carlo (SMC) methods.
A MAB is a sequential decision making problem where the goal is to learn a policy that maximizes long term payoff, where only the reward of the executed action is observed. In the stochastic MAB, the reward for each action is generated from an unknown distribution, often assumed to be stationary. To decide which action …

abstract algorithms arxiv bayesian beyond cs.lg decision decision making learn making policy stat.co stat.ml type