A script to demonstrate Support Vector Machine (SVM) classification on outcomes of the battles of Robocode.
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Peter Babič 867f5cbd23 MIT License 4 years ago
data same robots classification 9 years ago
extras same robots classification 9 years ago
.gitattributes scaling of outputs working 9 years ago
.gitignore reworked menu 9 years ago
LICENSE MIT License 4 years ago
README.md fix links and pamac 4 years ago
robocode-svm useless cat fix 9 years ago



robocode-svm is a script to demonstrate Support Vector Machine (SVM) classification on outcomes of the battles of Robocode. You can check the original assignment that led to this work here.


The script runs under Linux and requires robocode, svm-scale, svm-train and svm-predict executables located in the PATH. On Arch Linux (or it's derivative, like i.e. Manjaro), this can be achieved by installing libsvm and robocode from AUR (if enabled) by following command (or equivalent):

pamac install robocode libsvm


Generate the data required for SVM classification and show the accuracy
of the generated SVM model. If coordinates are provided, one visual battle
is shown to support the prediction.

USAGE: robocode-svm ACTION
USAGE: robocode-svm --battle x y [alpha]

     --battle		Shows visual run of the specified battle
  -g,--generate		Just generate the battle data
  -a,--accuracy		Show the accuracy of the SVM model,
  -c,--clean		Delete all the battle data!
  -h,--help		Show this help


The robots on test are sample.Corners (starting in center) and sample.TrackFire (roaming). The gun cooling rate is set to 0.07. These were obtained by experimenting and provide great outcomes on the battlefied, that can be classified easily.

SVM kernel parameters

The SVM type used is C-SVC (multi-class classification), with the radial basis kernel. The cost parameter C is 10 and the sigma parameter is 2. You can find more details about these at libsvm home.

2D visualisation

When the third parameter (initial gun orientation) of the roaming robot is omitted, we can generate sample visualisation of the result of this classification. Our experiments were indicating, that this parameter has low (about 2%) significance on the outcome of the battle.

2D visualisation

The visualisation color shows, which robot is likely to win, when the roaming one starts on given position on the battlefield. You can estimate the coordinates on the image and run script with the --battle parameter to prove the classification.