Answers.Body: <p>There is a small survey of continuous states, actions and time in reinforcement learning in my <a href="https://lume.ufrgs.br/handle/10183/157591" rel="nofollow noreferrer">thesis proposal</a>.</p>
  
  <p>Regarding books, <a href="https://books.google.com.br/books/about/Reinforcement_Learning.html?id=YPjNuvrJR0MC&amp;redir_esc=y" rel="nofollow noreferrer">Reinforcement Learning: State-of-the-Art</a> seems to be pretty up-to-date from the excerpts I've read.</p>
  
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Answers.CreationDate: 2016-08-24T16:25:01.740Z
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Body: <p>Most introductions to the field of MDPs and Reinforcement learning focus exclusively on domains where space and action variables are integers (and finite). This way we are introduced quickly to Value Iteration, Q-Learning, and the like.</p>
  
  <p>However, the most interesting applications (say, <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.3518&amp;rep=rep1&amp;type=pdf" rel="nofollow noreferrer">flying helicopters</a>) of RL and MDPs involve continuous state space and action spaces. I'd like to go beyond basic introductions and focus on these cases but I am not sure how to get there. </p>
  
  <p>What are some resources on continuous state and action spaces MDPs for reinforcement learning? And what areas do I need to know or study to understand these cases in-depth?</p>
  
Tags: <reinforcement-learning><reference-request><research><markov-decision-process>
Title: What are some resources on continuous state and action spaces MDPs for reinforcement learning?
UserDisplayName: CarrKnight
ThreadText: # What are some resources on continuous state and action spaces MDPs for reinforcement learning?
  
  (Asked by: username_0 on 2016-08-24)
  
  Most introductions to the field of MDPs and Reinforcement learning focus exclusively on domains where space and action variables are integers (and finite). This way we are introduced quickly to Value Iteration, Q-Learning, and the like.
  
  However, the most interesting applications (say, [flying helicopters](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.3518&rep=rep1&type=pdf)) of RL and MDPs involve continuous state space and action spaces. I'd like to go beyond basic introductions and focus on these cases but I am not sure how to get there.
  
  What are some resources on continuous state and action spaces MDPs for reinforcement learning? And what areas do I need to know or study to understand these cases in-depth?
  
  **Tags:** reinforcement-learning, reference-request, research, markov-decision-process
  
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  ## 1 answers
  
  ### Answer by username_1 on 2016-08-24. Score: 6 | Accepted answer
  
  There is a small survey of continuous states, actions and time in reinforcement learning in my [thesis proposal](https://lume.ufrgs.br/handle/10183/157591).
  
  Regarding books, [Reinforcement Learning: State-of-the-Art](https://books.google.com.br/books/about/Reinforcement_Learning.html?id=YPjNuvrJR0MC&redir_esc=y) seems to be pretty up-to-date from the excerpts I've read.
  
  
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