Body: <p>I've heard before that policy gradient and Q-learning approaches fail on games of imperfect information. I was watching <a href="https://www.youtube.com/watch?v=U0mxx7AoNz0" rel="nofollow noreferrer">this video</a> (starting at 23:45) about the Player of Games AI, but I couldn't understand why AlphaZero (with minor tweaks) couldn't be used successfully for playing games of imperfect information.</p>
  <p>My intuition tells me that you should be able to reinforce actions that lead to high reward and discourage actions that lead to low rewards. I would expect these reinforcements and discouragements to even out over time to the optimal value for cases where there was some aspect of state missing.</p>
  <p>Is there a satisfying answer for why this isn't the case?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-22T22:14:29.433Z
Comments.Text: Hi @Jackeown! Good question! The linked video is very long. If possible, please edit the question to include the time where the video states "why AlphaZero (with minor tweaks) couldn't be used successfully for playing games of imperfect information". That might help other users save time before answering the question.
Comments.UserDisplayName: DeepQZero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-24T20:50:36.443Z
Comments.Text: @DeepQZero I added a timestamp.
Comments.UserDisplayName: JacKeown
Tags: <reinforcement-learning><policy-gradients><alphazero><imperfect-information>
Title: Why do policy gradient methods not work for imperfect information games?
UserDisplayName: JacKeown
ThreadText: # Why do policy gradient methods not work for imperfect information games?
  
  (Asked by: username_0 on 2022-12-21)
  
  I've heard before that policy gradient and Q-learning approaches fail on games of imperfect information. I was watching [this video](https://www.youtube.com/watch?v=U0mxx7AoNz0) (starting at 23:45) about the Player of Games AI, but I couldn't understand why AlphaZero (with minor tweaks) couldn't be used successfully for playing games of imperfect information.
  
  My intuition tells me that you should be able to reinforce actions that lead to high reward and discourage actions that lead to low rewards. I would expect these reinforcements and discouragements to even out over time to the optimal value for cases where there was some aspect of state missing.
  
  Is there a satisfying answer for why this isn't the case?
  
  **Tags:** reinforcement-learning, policy-gradients, alphazero, imperfect-information
  > **Comments:**
  > **username_1**: Hi @Jackeown! Good question! The linked video is very long. If possible, please edit the question to include the time where the video states "why AlphaZero (with minor tweaks) couldn't be used successfully for playing games of imperfect information". That might help other users save time before answering the question.  
  > **username_0**: @DeepQZero I added a timestamp.  
  
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  ## 0 answers
  
  