Answers.Body: <p>Yes and we can do it in a similar way we normally perform hyper-param optimization. See <a href="https://deepai.org/publication/online-hyper-parameter-tuning-in-off-policy-learning-via-evolutionary-strategies" rel="nofollow noreferrer">this paper from Google</a></p>
  <p>In this framework we can also attempt to jerry-rig some of our classic algorithmic tuning techniques with better or worse success depending on the context.</p>
  
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Answers.CreationDate: 2022-07-21T09:06:14.180Z
Answers.LastActivityDate: 2022-07-21T09:06:14.180Z
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Body: <p>I am interested in learning about hyper-parameter tuning for off-policy reinforcement learning (specifically DQN). Could someone point me to papers published or empirical observations in this area?</p>
  
Tags: <reinforcement-learning><dqn><off-policy-methods>
Title: Has there been a study done in tuning hyper-parameters for off-policy reinforcement learning?
UserDisplayName: desert_ranger
ThreadText: # Has there been a study done in tuning hyper-parameters for off-policy reinforcement learning?
  
  (Asked by: username_0 on 2022-07-21)
  
  I am interested in learning about hyper-parameter tuning for off-policy reinforcement learning (specifically DQN). Could someone point me to papers published or empirical observations in this area?
  
  **Tags:** reinforcement-learning, dqn, off-policy-methods
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-07-21. Score: 1 | Accepted answer
  
  Yes and we can do it in a similar way we normally perform hyper-param optimization. See [this paper from Google](https://deepai.org/publication/online-hyper-parameter-tuning-in-off-policy-learning-via-evolutionary-strategies)
  
  In this framework we can also attempt to jerry-rig some of our classic algorithmic tuning techniques with better or worse success depending on the context.
  
  
  ---
  
  