<p>Actually, distributional RL is a well-studied field in deep RL. Generally speaking, distributional RL needs more computing sources (1.1X), because of the quantile head. We can also find new distributional RL literature in NeurIPS2020 :).</p>

CC BY-SA 4.0
2021-05-28T09:01:46.843Z
Hello. Welcome to AI SE. Maybe you can provide 2-3 examples of papers on Distributional RL that were published in NeurIPS 2020.
nbro
CC BY-SA 4.0
2021-05-28T01:37:41.890Z
2021-05-28T01:37:41.890Z
Jason King
<p>I read some papers (for example, <a href="https://arxiv.org/abs/1707.06887" rel="nofollow noreferrer">this</a>) and blogs that spoke about the advantages of distributional Q learning. However, it no longer seems to come up in literature. Did it have any shortcomings that led to its failure? If yes, can someone can talk about it here?</p>

<reinforcement-learning><reference-request><deep-rl>
Why did Distributional Q Learning go out of popularity?
desert_ranger
# Why did Distributional Q Learning go out of popularity?

(Asked by: username_0 on 2021-05-28)

I read some papers (for example, [this](https://arxiv.org/abs/1707.06887)) and blogs that spoke about the advantages of distributional Q learning. However, it no longer seems to come up in literature. Did it have any shortcomings that led to its failure? If yes, can someone can talk about it here?

**Tags:** reinforcement-learning, reference-request, deep-rl


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## 1 answers

### Answer by username_1 on 2021-05-28. Score: 1 | Accepted answer

Actually, distributional RL is a well-studied field in deep RL. Generally speaking, distributional RL needs more computing sources (1.1X), because of the quantile head. We can also find new distributional RL literature in NeurIPS2020 :).

> **Comments:**
> **username_2**: Hello. Welcome to AI SE. Maybe you can provide 2-3 examples of papers on Distributional RL that were published in NeurIPS 2020.  

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