Langevin Monte Carlo for Contextual Bandits. (arXiv:2206.11254v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11254

We study the efficiency of Thompson sampling for contextual bandits. Existing
Thompson sampling-based algorithms need to construct a Laplace approximation
(i.e., a Gaussian distribution) of the posterior distribution, which is
inefficient to sample in high dimensional applications for general covariance
matrices. Moreover, the Gaussian approximation may not be a good surrogate for
the posterior distribution for general reward generating functions. We propose
an efficient posterior sampling algorithm, viz., Langevin Monte Carlo Thompson
Sampling (LMC-TS), that uses Markov Chain Monte Carlo …

arxiv lg