Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors


Junya Honda, Akimichi Takemura ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:375-383, 2014.


In stochastic bandit problems, a Bayesian policy called Thompson sampling (TS) has recently attracted much attention for its excellent empirical performance. However, the theoretical analysis of this policy is difficult and its asymptotic optimality is only proved for one-parameter models. In this paper we discuss the optimality of TS for the model of normal distributions with unknown means and variances as one of the most fundamental examples of multiparameter models. First we prove that the expected regret of TS with the uniform prior achieves the theoretical bound, which is the first result to show that the asymptotic bound is achievable for the normal distribution model. Next we prove that TS with Jeffreys prior and reference prior cannot achieve the theoretical bound. Therefore choice of priors is important for TS and non-informative priors are sometimes risky in cases of multiparameter models.

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