Answers.Body: <p>Yes, it is possible. The field of <em>deep reinforcement learning</em> is all about using deep neural networks (that is, neural networks with at least one hidden layer) to <em>approximate</em> value functions (such as the <span class="math-container">$Q$</span> function) or policies.</p>
  
  <p>Have a look at the paper <a href="https://arxiv.org/abs/1708.05866" rel="nofollow noreferrer">A Brief Survey of Deep Reinforcement Learning</a> that gives a brief survey of the field.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-21T19:15:49.277Z
Answers.LastActivityDate: 2019-04-21T19:15:49.277Z
Answers.UserDisplayName: nbro
Answers.Body: <p><a href="http://karpathy.github.io/2016/05/31/rl/" rel="nofollow">Andrej Karpathy's blog</a> has a tutorial on getting a neural network to learn pong with reinforcement learning. His commentary on the current state of the field is interesting.</p>
  
  <p>He also provides a whole bunch of links (David Silver's <a href="http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html" rel="nofollow">course</a> catches my eye). <a href="https://www.youtube.com/watch?v=2pWv7GOvuf0" rel="nofollow">Here is a working link to the lecture videos.</a></p>
  
  <p>Here are demos of DeepMinds game playing.
  Get links to the papers at Andrej Karpathy's blog above
  - <a href="https://www.youtube.com/watch?v=MAMuNUixKJ8" rel="nofollow">rat fps</a></p>
  
  <ul>
  <li><a href="http://livestream.com/oxuni/StracheyLectureDrDemisHassabis/videos/113380152" rel="nofollow">nice demos at 19 minutes into this</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-07T06:30:33.053Z
Answers.LastActivityDate: 2016-08-08T08:34:38.777Z
Answers.LastEditDate: 2016-08-08T08:34:38.777Z
Answers.UserDisplayName: Conor Cosnett
Body: <p>I've implemented <a href="https://en.wikipedia.org/wiki/Reinforcement_learning" rel="nofollow noreferrer">the reinforcement learning algorithm</a> for an agent to play <a href="https://github.com/admonkey/snappybird" rel="nofollow noreferrer">snappy bird</a> (a shameless cheap ripoff of flappy bird) utilizing a q-table for storing the history for future lookups. It works and eventually achieves perfect convergence after enough training.</p>
  
  <p>Is it possible to implement a neural network to do function approximation in order to accomplish the purpose of the q-table? Obviously, storage is a concern with the q-table, but it doesn't seem to ever train with the neural net alone. Perhaps training the NN on an existing q-table would work, but I would like to not use a q-table at all if possible.</p>
  
Tags: <neural-networks><reinforcement-learning><deep-rl><function-approximation>
Title: Is it possible to implement reinforcement learning using a neural network?
UserDisplayName: Jeff Puckett
ThreadText: # Is it possible to implement reinforcement learning using a neural network?
  
  (Asked by: username_0 on 2016-08-02)
  
  I've implemented [the reinforcement learning algorithm](https://en.wikipedia.org/wiki/Reinforcement_learning) for an agent to play [snappy bird](https://github.com/admonkey/snappybird) (a shameless cheap ripoff of flappy bird) utilizing a q-table for storing the history for future lookups. It works and eventually achieves perfect convergence after enough training.
  
  Is it possible to implement a neural network to do function approximation in order to accomplish the purpose of the q-table? Obviously, storage is a concern with the q-table, but it doesn't seem to ever train with the neural net alone. Perhaps training the NN on an existing q-table would work, but I would like to not use a q-table at all if possible.
  
  **Tags:** neural-networks, reinforcement-learning, deep-rl, function-approximation
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2016-08-07. Score: 2 | Accepted answer
  
  [Andrej Karpathy's blog](http://karpathy.github.io/2016/05/31/rl/) has a tutorial on getting a neural network to learn pong with reinforcement learning. His commentary on the current state of the field is interesting.
  
  He also provides a whole bunch of links (David Silver's [course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) catches my eye). [Here is a working link to the lecture videos.](https://www.youtube.com/watch?v=2pWv7GOvuf0)
  
  Here are demos of DeepMinds game playing.
  Get links to the papers at Andrej Karpathy's blog above
  - [rat fps](https://www.youtube.com/watch?v=MAMuNUixKJ8)
  
  * [nice demos at 19 minutes into this](http://livestream.com/oxuni/StracheyLectureDrDemisHassabis/videos/113380152)
  
  
  ---
  
  ### Answer by username_1 on 2019-04-21. Score: 0
  
  Yes, it is possible. The field of *deep reinforcement learning* is all about using deep neural networks (that is, neural networks with at least one hidden layer) to *approximate* value functions (such as the $Q$ function) or policies.
  
  Have a look at the paper [A Brief Survey of Deep Reinforcement Learning](https://arxiv.org/abs/1708.05866) that gives a brief survey of the field.
  
  
  ---
  
  