Answers.Body: <p>There isn't really one specific method which makes any RL agent have faster learning. Rather there is a long list of methods which have shown to increase the speed of learning and they can sometimes play nicely with each other. </p>
  
  <p>Some examples:</p>
  
  <ol>
  <li><a href="http://www-anw.cs.umass.edu/~barto/courses/cs687/Sutton-Precup-Singh-AIJ99.pdf" rel="nofollow noreferrer">Options</a></li>
  <li><a href="https://arxiv.org/abs/1512.04087" rel="nofollow noreferrer">True Online Methods</a></li>
  <li><a href="https://arxiv.org/abs/1602.01783" rel="nofollow noreferrer">Asynchronous Methods</a></li>
  </ol>
  
  <p>These are the most influential and promising methods I can think of but the list of techniques is not limited to these 3. </p>
  
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Answers.UserDisplayName: Jaden Travnik
Answers.Body: <p>There is a very interesting learning agent. They call it Neural-Episodic-Control. Here is the link for the paper: <a href="https://arxiv.org/abs/1703.01988" rel="nofollow noreferrer">https://arxiv.org/abs/1703.01988</a> . Their experiments show that NEC requires an order of magnitude fewer interactions with the environment than agents previously proposed. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-09-22T16:51:22.563Z
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Answers.UserDisplayName: Molnár István
Body: <p>In other words, which existing reinforcement method learns with fewest episodes? <a href="http://www.jmlr.org/papers/volume3/brafman02a/brafman02a.pdf" rel="nofollow noreferrer">R-Max</a> comes to mind, but it's very old and I'd like to know if there is something better now.</p>
  
Tags: <reinforcement-learning><reference-request><algorithm-request><state-of-the-art><sample-efficiency>
Title: What is the current state-of-the-art in Reinforcement Learning regarding data efficiency?
UserDisplayName: rcpinto
ThreadText: # What is the current state-of-the-art in Reinforcement Learning regarding data efficiency?
  
  (Asked by: username_0 on 2016-08-06)
  
  In other words, which existing reinforcement method learns with fewest episodes? [R-Max](http://www.jmlr.org/papers/volume3/brafman02a/brafman02a.pdf) comes to mind, but it's very old and I'd like to know if there is something better now.
  
  **Tags:** reinforcement-learning, reference-request, algorithm-request, state-of-the-art, sample-efficiency
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2017-09-22. Score: 0
  
  There isn't really one specific method which makes any RL agent have faster learning. Rather there is a long list of methods which have shown to increase the speed of learning and they can sometimes play nicely with each other.
  
  Some examples:
  
  1. [Options](http://www-anw.cs.umass.edu/~barto/courses/cs687/Sutton-Precup-Singh-AIJ99.pdf)
  2. [True Online Methods](https://arxiv.org/abs/1512.04087)
  3. [Asynchronous Methods](https://arxiv.org/abs/1602.01783)
  
  These are the most influential and promising methods I can think of but the list of techniques is not limited to these 3.
  
  
  ---
  
  ### Answer by username_2 on 2017-09-22. Score: 0
  
  There is a very interesting learning agent. They call it Neural-Episodic-Control. Here is the link for the paper: <https://arxiv.org/abs/1703.01988> . Their experiments show that NEC requires an order of magnitude fewer interactions with the environment than agents previously proposed.
  
  
  ---
  
  