Football Table RL: Difference between revisions
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==Reinforcement Learning== | ==Reinforcement Learning== | ||
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>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> | ||
<references></references> | <references></references> | ||
==Value Function Approximation== | ==Value Function Approximation== | ||
Revision as of 16:25, 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). [ http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Reinforcement Learning: an introduction]