<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>

CC BY-SA 4.0
2022-07-21T09:06:14.180Z
2022-07-21T09:06:14.180Z
hisairnessag3
<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>

<reinforcement-learning><dqn><off-policy-methods>
Has there been a study done in tuning hyper-parameters for off-policy reinforcement learning?
desert_ranger
# 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.


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