This package contains the learning code to accompany the Keepaway
benchmark player framework.

This is a particular snapshot of the code, and different published
results have used different learning algorithms.  We don't guarantee
that this particular instantiation will line up with all of the
published papers using keepaway.  Variations on the learning algorithm
are described in the papers indexed here:
http://www.cs.utexas.edu/~pstone/Papers/bib2html/

We provide this code as-is with no support implied. This code has not
been tested and we make no guarantees that it will even compile.

The learning code was created by

 Peter Stone
 Gregory Kuhlmann
 Matthew E. Taylor
 Yaxin Liu
 and Shivaram Kalyanakrishnan

in the Department of Computer Sciences
at the University of Texas at Austin:
http://www.cs.utexas.edu/~AustinVilla/sim/keepaway/

This package was created as part of our research using
the keepaway domain.  The following is a list of selected 
publications:

Keepaway Soccer: From Machine Learning Testbed to Benchmark.
Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, and Yaxin Liu.
In Itsuki Noda, Adam Jacoff, Ansgar Bredenfeld, and Yasutake Takahashi, editors, RoboCup-2005: Robot Soccer World Cup IX, Springer Verlag, Berlin, 2006. To appear.

Reinforcement Learning for RoboCup-Soccer Keepaway.
Peter Stone, Richard S. Sutton, and Gregory Kuhlmann.
Adaptive Behavior, 2005. 

We ask that you please cite these papers if you publish work that builds
off of this code.

The players are built upon version 3.3 of the UvA Basic team that is 
publicly available from:
http://staff.science.uva.nl/~jellekok/robocup/2003/index_en.html

The communication code is based on:
saymsglib - a library to generate and parse messages in the Coachable agent
communication standard created by Carnegie Mellon University.  It is 
described here:
http://www-2.cs.cmu.edu/~robosoccer/simulator/comm_standard.html

REQUIREMENTS:

The Keepaway Player Framework
http://www.cs.utexas.edu/~AustinVilla/sim/keepaway/

The RoboCup Soccer Server, available from:
http://sserver.sf.net

