Adaptive Sampling Technique using KullbackLeibler distance

Contributers

Arun Vignesh Malarkkan
Gowtham Sekkilar
Raghavendran Ramakrishnan
Faisal Alatawi

Arizona State University Dec 3, 2018

There are two modules present in this project:

1. Tracking

This application is used to demonstrate the ability of the sampling technique to score in game of Ghostbuster.

2. Ghostbuster

This is modified version of programming assignment for CS 188 in UC Berkeley. We altered the dynamic ghost buster game to use the Adaptive sampling.The GUI also provides a visual representation of sample size. We have also added a feature to view the sample size dynamically in the GUI.

To run the tracking code:

-To test the code:
 python KLD_run.py

-To run q4 (from project 4) :
python KLD_run.py  -q q4

- note add (--no-graphics) to turn off the graphics : python KLD_run.py  -q q4 --no-graphics

-To run q5 (from project 4):
python KLD_run.py  -q q5

*To run the ghostbuster app:

Switch to the ghostbuster project

 -To run the ghostbuster game:

  python ghostbusters.py -w -m center -i approximate -k 1 --fixrandomseed -n 0.3 -l medium -n 0.8