  In this work, we initiate the idea of using denoising diffusion models to
learn priors for online decision making problems. Our special focus is on the
meta-learning for bandit framework, with the goal of learning a strategy that
performs well across bandit tasks of a same class. To this end, we train a
diffusion model that learns the underlying task distribution and combine
Thompson sampling with the learned prior to deal with new tasks at test time.
Our posterior sampling algorithm is designed to carefully balance between the
learned prior and the noisy observations that come from the learner's
interaction with the environment. To capture realistic bandit scenarios, we
also propose a novel diffusion model training procedure that trains even from
incomplete and/or noisy data, which could be of independent interest. Finally,
our extensive experimental evaluations clearly demonstrate the potential of the
proposed approach.
