WaveSim

Background

To facilitate a new wave of gun research, I'm setting up a system that will let me easily test a gun's classification against pre-gathered wave data. This would take the place of running a zillion Robocode battles.

Benefits:

• By removing all the overhead of running actual battles, I think this will allow a given gun test to run an order of magnitude faster, a huge boon to the pursuit of wacky targeting techniques.
• It will make it more feasible to experiment with sophisticated algorithms that are an order of magnitude slower than what we usually use, as some clustering algorithms are.
• I could record data from actual battles instead of being limited to TCs, since every classification run would be against the same exact data, anyway.
• I can see hit percentages (normalized or not) or energy ratios (spent vs gained vs damage), which I think are a more direct measurement of a gun's accuracy than TC results.

Testing classification algorithms against raw data sets is nothing new, but it's high time I actually tried it for Robocode gun research. =) Of course, this will only work against non-adaptive movements. (Some of this process is similar to my old Segmentation Research.)

Downloads

WaveSim

• WaveSim 1.1 - Source, .class files, and Javadoc to simulate battles using raw wave data.

I'm something of a noob with packaging a regular Java program, but hopefully it will be cleaned up in the future. If you put this on your build path, you should be able to set WaveRunner.BASEDIR, implement the Classifier interface, and start running battles. The doc directory in the .jar describes the only three classes you need to know: WaveRunner, WaveRecord, and the Classifier interface.

There is one sample data file in the .jar at voidious/test/wavesim/jam.mini.Raiko 0.43_1_wavedata.txt. If you unzip the .jar, you could run: java -cp ~/robocode/libs/robocode.jar:. voidious.test.wavesim.WaveRunner - this will run a battle against the sample data file using the Head-On Targeting classifier and the KNN classifier from Diamond's main gun. (The latter is what needs Robocode .jar on the classpath.)

TripHammer RES

These two versions of TripHammer will output comma delimited data files from each wave collected during battles. They are in the format used by WaveRunner. Make sure to up your disk quota in robocode.properties. (robocode.robot.filesystem.quota=2000000000 will give you 2 gigs.)

• TripHammer RES1.6 - Diamond's movement and main gun.
• TripHammer RES1.6T - Diamond's main gun in TC mode.

Components

Data-gathering bot

First step is collecting some data. I modified my TripHammer code to output every wave it gathers in a .csv type format. 100 seasons of TCRM is about 4 gigs.

Battle simulation

I have utility classes to load wave data, step through it as a real battle would, feed waves to a classifier for learning, classify each firing wave / check for hits, and record overall hit rates, energy, and damage. This is basically simulating a battle from the perspective of a wave-based gun.

This is done, though it could use some bells and whistles.

Classifiers

I have a simple interface implemented by each classifier - initialize, feed it a wave, classify a wave. I've decided not to muddy up my main code base by making my guns work interchangeably with my classifier test code, but porting between them is pretty simple. Ported Diamond's main gun in a few minutes.

Blog

3/17/2010

Posted first public version of my WaveSim utility. Have fun. =)

Of course I quickly realized that I overlooked something. The WaveRecord class also needs to be documented. And this uses precise MEA and wall distance based on precise MEA. I should add options for traditional MEA and orbital wall distance.

3/12/2010 pm

Super busy with work, so I can't focus on this much, but I've got the basics working now. For 100 seasons of TCRM:

TripHammer KNN classifier:
Took 1262.0 seconds
Hit percentage: 11.813935415920957
Energy ratio: 0.983354371037666

HOT classifier:
Took 341.0 seconds
Hit percentage: 8.739709205199102
Energy ratio: 0.7279814945288104

HOT score seems pretty high, but it gets 5.9% vs DT, which seems right. At avg distance of 500, you'd get about 5% against a perfectly flat movement.

Best of all is < 1 second per battle for Diamond's main gun. A real TCRM is 15-20 seconds per battle for this gun. Still have some obvious little optimizations to add and there are probably some bigger ones once I focus on it.

3/12/2010 am

I'll post all this soon (except the 4 gigs of data), if anyone's interested in tinkering with it. I may also post info about some of the clustering schemes I've tried. An example of one of the really intensive clustering algorithms that I find interesting is QT clustering. A couple of my previous experiments were inspired by that one.