[robocup-small] Keepaway learning testbed release

[Gregory Kuhlmann]

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Hi all,

I am pleased to announce the release of our Keepaway benchmark player
framework source code for use with the 2D soccer server used at RoboCup.
In case you are unfamiliar with it, the keepaway domain is a subtask of
soccer in which a team of "keepers" tries to maintain possession of the
ball within a small playing region, while the opposing team of "takers"
tries to take the ball away.

This domain has been used successfully in the past as a machine learning
testbed.  See the following publications for more information:

  Peter Stone, Richard S. Sutton, and Gregory Kuhlmann.
  Scaling Reinforcement Learning toward RoboCup Soccer.
  http://www.cs.utexas.edu/~kuhlmann/papers/b2hd-AB2004-scaling.html

  Gregory Kuhlmann and Peter Stone.
  Progress in Learning 3 vs. 2 Keepaway.
  http://www.cs.utexas.edu/~kuhlmann/papers/b2hd-LNAI2003-keepaway.html

Version 0.1 of the framework is available for download from our keepaway
website:

http://www.cs.utexas.edu/~AustinVilla/sim/keepaway/

The package contains the source code for the Keepaway benchmark player
framework and the source code for some keepaway utility programs.  The
framework includes all low- and mid-level keepaway behaviors.  A few
example high-level policies are included.  Not included is any learning
code.  However, the framework was designed to make it easy to insert your
own learning code.

Also included on our page are two tutorials.  The first tutorial explains
step-by-step how to install the players, run a simulation, and generate a
graph of the learning curve. The second tutorial explains how to modify
the players to include your own learning code.

Please let us know if you have any questions.  Also, we would like to hear
if and how you are using this code.

Cheers,
Greg Kuhlmann and
Peter Stone