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A study of Thompson Sampling with Parameter h (1710.02174v1)
Published 5 Oct 2017 in cs.LG, cs.IT, and math.IT
Abstract: Thompson Sampling algorithm is a well known Bayesian algorithm for solving stochastic multi-armed bandit. At each time step the algorithm chooses each arm with probability proportional to it being the current best arm. We modify the strategy by introducing a paramter h which alters the importance of the probability of an arm being the current best arm. We show that the optimality of Thompson sampling is robust to this perturbation within a range of parameter values for two arm bandits.