Football Table RL: Difference between revisions

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==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). <cite>[ http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Reinforcement Learning: an introduction] </cite></p>
<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). <ref>[ http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Reinforcement Learning: an introduction] </ref></p>
<references></references>
<references/>


==Value Function Approximation==
==Value Function Approximation==

Revision as of 15:26, 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). [1]

  1. [ http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Reinforcement Learning: an introduction]

Value Function Approximation