Football Table RL: Difference between revisions
Jump to navigation
Jump to search
| Line 1: | Line 1: | ||
==Reinforcement Learning== | ==Reinforcement Learning== | ||
<p>The football table employs on-line value iteration, namely Greedy-GQ<math>(\lambda)</math> and Approximate-Q<math>(\lambda)</math>. This page does not explain Reinforcement learning theory, it just touches on the usage and implementation of the provided library (libvfa located on the SVN). Too get a basic understanding of Reinforcement Learning i suggest reading the book by Sutton & Barto <ref> | <p>The football table employs on-line value iteration, namely Greedy-GQ<math>(\lambda)</math> and Approximate-Q<math>(\lambda)</math>. This page does not explain Reinforcement learning theory, it just touches on the usage and implementation of the provided library (libvfa located on the SVN). Too get a basic understanding of Reinforcement Learning i suggest reading the book by Sutton & Barto <ref>Reinforcement Learning: an introduction, http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html </ref>. For more on using function approximation, i suggest the book by Lucian Busoniu et al. <ref>Reinforcement Learning and Dynamic Programming using Function Approximation,http://www.crcnetbase.com/isbn/9781439821091</ref>, which is freely available as e-book from within the TU/e network.</p> | ||
<references/> | <references/> | ||
==Value Function Approximation== | ==Value Function Approximation== | ||
Revision as of 16:38, 11 September 2013
Reinforcement Learning
The football table employs on-line value iteration, namely Greedy-GQ[math]\displaystyle{ (\lambda) }[/math] and Approximate-Q[math]\displaystyle{ (\lambda) }[/math]. This page does not explain Reinforcement learning theory, it just touches on the usage and implementation of the provided library (libvfa located on the SVN). Too get a basic understanding of Reinforcement Learning i suggest reading the book by Sutton & Barto [1]. For more on using function approximation, i suggest the book by Lucian Busoniu et al. [2], which is freely available as e-book from within the TU/e network.
- ↑ Reinforcement Learning: an introduction, http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
- ↑ Reinforcement Learning and Dynamic Programming using Function Approximation,http://www.crcnetbase.com/isbn/9781439821091