# What are the state-of-the-art learning algorithms for contextual bandits with stochastic rewards

I am building a solution for an environment with stochastic rewards in an online setting. I am wondering what the state of the art is in this setting. Is it $$\epsilon$$-greedy (with logistic regression), LinUCB (with ridge regression), Thompson Sampling (with some approximator)? Could maybe point me to the relevant papers/articles?