Answers.Body: <p>There is this paper <a href="https://arxiv.org/pdf/1712.00654.pdf" rel="nofollow noreferrer">Representation and Reinforcement Learning for
  Personalized Glycemic Control in Septic Patients</a>, presented in the Machine Learning for Health Workshop in NIPS 2017. Here is a quote from the paper where the authors describe the clustering approach: </p>
  
  <blockquote>
    <p>After we generated the state representation,
    we used the k-means clustering algorithm to categorize millions of patient states into 500 clusters
    such that similar clinical states can collapse into the same cluster.</p>
  </blockquote>
  
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Answers.CreationDate: 2020-05-11T23:11:17.973Z
Answers.LastActivityDate: 2020-05-11T23:11:17.973Z
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Body: <p>Are there any reference papers where it is used a KMeans-like algorithm in state space quantization in Reinforcement Learning instead of range buckets?</p>
  
Tags: <reinforcement-learning><reference-request><k-means><dimensionality>
Title: Can I do state space quantization using a KMeans-like algorithm instead of range buckets?
UserDisplayName: ddaedalus
ThreadText: # Can I do state space quantization using a KMeans-like algorithm instead of range buckets?
  
  (Asked by: username_0 on 2020-05-11)
  
  Are there any reference papers where it is used a KMeans-like algorithm in state space quantization in Reinforcement Learning instead of range buckets?
  
  **Tags:** reinforcement-learning, reference-request, k-means, dimensionality
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-05-11. Score: 2 | Accepted answer
  
  There is this paper [Representation and Reinforcement Learning for
  Personalized Glycemic Control in Septic Patients](https://arxiv.org/pdf/1712.00654.pdf), presented in the Machine Learning for Health Workshop in NIPS 2017. Here is a quote from the paper where the authors describe the clustering approach:
  
  > After we generated the state representation,
  > we used the k-means clustering algorithm to categorize millions of patient states into 500 clusters
  > such that similar clinical states can collapse into the same cluster.
  
  
  ---
  
  