Body: <p>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 <span class="math-container">$\epsilon$</span>-greedy (with logistic regression), LinUCB (with ridge regression), Thompson Sampling (with some approximator)? Could maybe point me to the relevant papers/articles?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-04T17:44:39.717Z
Comments.Text: If you consider contextual things, then LinUCB(Lin. 2011) is (still) SOTA. LinTS(Thompson Sampling) is not good as contextual-free TS. In non-stationary environment, you can consider discount(decay) approach of LinUCB: https://arxiv.org/abs/1909.09146
Comments.UserDisplayName: 구마왕
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-17T08:31:12.727Z
Comments.Text: @구마왕, thanks, good to know! How do you know what is state-of-the-art vs not (maybe a review paper or article?)
Comments.UserDisplayName: d56
Tags: <reinforcement-learning><state-of-the-art><algorithm-request><contextual-bandits>
Title: What are the state-of-the-art learning algorithms for contextual bandits with stochastic rewards
UserDisplayName: d56
ThreadText: # What are the state-of-the-art learning algorithms for contextual bandits with stochastic rewards
  
  (Asked by: username_0 on 2021-04-26)
  
  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?
  
  **Tags:** reinforcement-learning, state-of-the-art, algorithm-request, contextual-bandits
  
  > **Comments:**
  > **username_1**: If you consider contextual things, then LinUCB(Lin. 2011) is (still) SOTA. LinTS(Thompson Sampling) is not good as contextual-free TS. In non-stationary environment, you can consider discount(decay) approach of LinUCB: https://arxiv.org/abs/1909.09146  
  > **username_0**: @username_1, thanks, good to know! How do you know what is state-of-the-art vs not (maybe a review paper or article?)  
  
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  ## 0 answers
  
  