Answers.Body: <p>One renowned example for the specified case is <a href="https://arxiv.org/abs/1609.05473" rel="nofollow noreferrer">SeqGAN</a></p>
  <blockquote>
  <p>Modeling <strong>the data generator as a stochastic policy in reinforcement learning (RL)</strong>, SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. <strong>The RL reward signal comes from the GAN discriminator judged on a complete sequence</strong>, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-05-26T18:48:19.960Z
Answers.LastActivityDate: 2021-05-26T18:48:19.960Z
Answers.UserDisplayName: OmG
Body: <p>Can we use reinforcement learning for sequence-to-sequence tasks? If yes, whether or not this is a good choice, how could this be done?</p>
  
Tags: <reinforcement-learning><reference-request><applications><sequence-modeling><seq2seq>
Title: Can Reinforcement Learning be used to generate sequences?
UserDisplayName: penguin_smasher
ThreadText: # Can Reinforcement Learning be used to generate sequences?
  
  (Asked by: username_0 on 2021-05-26)
  
  Can we use reinforcement learning for sequence-to-sequence tasks? If yes, whether or not this is a good choice, how could this be done?
  
  **Tags:** reinforcement-learning, reference-request, applications, sequence-modeling, seq2seq
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-05-26. Score: 3
  
  One renowned example for the specified case is [SeqGAN](https://arxiv.org/abs/1609.05473)
  
  > Modeling **the data generator as a stochastic policy in reinforcement learning (RL)**, SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. **The RL reward signal comes from the GAN discriminator judged on a complete sequence**, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.
  
  
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