Body: <p>Could someone clear my doubt on the loss function used in <a href="https://arxiv.org/pdf/1609.05473.pdf" rel="nofollow noreferrer">SeqGAN paper</a> . The paper uses policy gradient method to train the generator which is a recurrent neural network here.</p>
  <ol>
  <li>Have I interpreted the terms correctly?</li>
  <li>What are we summing over? The entire vocabulary of words?</li>
  </ol>
  <p>Loss function - my interpretation:
  <a href="https://i.sstatic.net/4ux3u.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/4ux3u.png" alt="enter image description here" /></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-31T15:09:37.123Z
Comments.Text: Try to ask a more specific question and simplify the post. In that way, I think you will attract more people and potentially someone that attempts to answer your question. If you have multiple questions, I suggest that you create 1 post for each of the questions (even if they are related) for the simple reason that simplicity/clarity often attracts more people.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-06T17:46:19.537Z
Comments.Text: @nbro Thanks for the suggestion. I removed the excess questions because my core doubt is what the term under the summation indicates
Comments.UserDisplayName: Mathav Raj
Tags: <reinforcement-learning><papers><generative-adversarial-networks><reinforce><seq2seq>
Title: SeqGAN - Policy gradient objective function interpretation
UserDisplayName: Mathav Raj
ThreadText: # SeqGAN - Policy gradient objective function interpretation
  
  (Asked by: username_0 on 2020-10-18)
  
  Could someone clear my doubt on the loss function used in [SeqGAN paper](https://arxiv.org/pdf/1609.05473.pdf) . The paper uses policy gradient method to train the generator which is a recurrent neural network here.
  
  1. Have I interpreted the terms correctly?
  2. What are we summing over? The entire vocabulary of words?
  
  Loss function - my interpretation:
  [![enter image description here](https://i.sstatic.net/4ux3u.png)](https://i.sstatic.net/4ux3u.png)
  
  **Tags:** reinforcement-learning, papers, generative-adversarial-networks, reinforce, seq2seq
  > **Comments:**
  > **username_1**: Try to ask a more specific question and simplify the post. In that way, I think you will attract more people and potentially someone that attempts to answer your question. If you have multiple questions, I suggest that you create 1 post for each of the questions (even if they are related) for the simple reason that simplicity/clarity often attracts more people.  
  > **username_0**: @nbro Thanks for the suggestion. I removed the excess questions because my core doubt is what the term under the summation indicates  
  
  ---
  
  ## 0 answers
  
  