Genetic tuning

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Genetic tuning

WaveSim looks great to genetic tune your gun classification. And would probably be the biggest improvement to Combat.

But what about movement and energy management? I wonder how people here tune them.

MN03:43, 16 January 2013

I've never done any genetic tuning outside of WaveSim for gun classification, but I've thought about it. At first it seems like I'd want to be able to, from Java code, modify some other Java code and package it into a Robocode bot, copy that bot into my RoboRunner robots dir, then run battles. That always seemed really cumbersome to me.

But I think a much simpler approach is to just have one packaged bot that you always run, it reads in the DNA string from a file and interprets it however you like, then your genetic algorithm code just modifies the input file and runs more battles. This is the approach I'd take. Once it's done, you can convert it to real hard-coded behavior if you like. Especially if you're tuning a MiniBot (or smaller), you'd probably want to remove the file reading code.

Voidious03:49, 16 January 2013

I tried this approach twice. Genetic tuning generated a file in the data directory, which was read by the bot during a battle. It works.

The challenge is working around the noise and slowness of full battles. There are techniques for dealing with noisy and slow fitness functions, and I tried some of them.

The naive approach would be running like 15 to 50 battles per chromosome and averaging the results, taking days to evaluate a single generation.

Another approach is running a single battle for each chromosome, them averaging the results for many near chromosomes (k-NN style). Assuming similar chromosomes give similar results, averaging them can suppress the noise while still giving a meaningful fitness evaluation. It took about 1 hour per generation. The technique is called "averaging over space".

Since the fitness landscape keeps changing due to more neighbours being added all the time, another technique called "random immigrants" is useful to avoid over-fitting. The technique is basically replacing a few chromosomes (the worst ones) with new random ones.

Also tried running a single battle per chromosome without averaging, but it was too unstable. Survival of the fittest became survival of the luckiest.

Another challenge is paralellizing fitness evaluation. JGAP's built-in parallelization is awful for the techniques above, so I built another one from scratch. Pick all chromosomes from a bulk fitness function, evaluate them in parallel (RoboRunner style), regroup/average all results and return.

Tried with Combat, tuning gun/movement/energy management against DrussGT alone, but failed due to over-specialization. Also tried with Impact, tuning energy management against the Rambot Challenge 2K6, this one worked ok.

WaveSim works around the problems above by using a fixed data set and a targeting simulation.

MN02:18, 17 January 2013

Unfortunately genetic tuning is very long and tedious if you do it with a whole robot. Genetics also happens to cheat, so you need a good sample size to test against. I have genetically evolved a robot that could perfectly defeat walls from a certain starting position for example. But no where else.

Chase19:29, 18 January 2013

That's pretty cool you tried it with overall robot behavior. That's definitely on another level in terms of complexity. I think that's usually the distinction between "Genetic Algorithms" (your code interprets the "DNA" string and alters its behavior in predefined ways) and "Genetic Programming" (your "DNA" string is translated into real code that itself is run as the bot's behavior).

Voidious23:11, 18 January 2013

It was the latter. It took about 100 generations (each generation with 1000 randomly mutated samples). I didn't take into consideration genetics natures. I thought it would evolve like how we write robots. You know a radar, some basic back and forth movement. Nope, everything spinned, it just happened it stumbled upon a perfect speed and turning ratio to move from its start location (to dodge all walls shots) and a perfect speed to spin its gun and fire to hit walls.

But this was from specific stating positions and angles, which I had programmed into the battle to remove all the extra 'noise' so I could run shorter battles and still get a good sample. Since neither walls or the robots genetics had any random function, I figured it was safe.

After all that work, I decided not to pursue it further. Since it was long boring, and I could write a better robot in 5 minutes.

Chase07:10, 19 January 2013

Preserving the 'noise' is useful in these situations to avoid over-fitting. Although using only Walls in the fitness function will still produce a bot which is only good against it.

MN14:23, 19 January 2013