GANN-EWN / Part 1 / GANNs (Genetic Algorithms applied to Neural Networks)

Combining Genetic Algorithms and Neural Networks is an idea that has been troubling my mind for the last 20 years. Unfortunately, life went in the way and I didn’t have a real chance of putting this idea into practice. Neural networks are very often used to do some “classifying” jobs, and they are very good at it. At least they got far beyond what we humans could program ourselves. However, as I have ideas for developing very interesting games, I also had in mind to have an AI play those games, and an AI that would actually learn how to play them, rather than programing it myself. This is exactly what the guys at DeepMind have been doing in the recent years. From Atari games to Go and Chess (and Shogi), they have amazed us all.

As I’m going back to my original interest in AI, I have taken upon myself to build an AI from scratch that would do exactly what I had in mind 20 years ago: learn how to play games.

In the meantime, things in the AI field have evolved. Graphics cards have opened new horizons for training bigger and faster Neural Networks. I have had some graphics cards that I used at some point to mine some cryptocurrencies for fun. Those cards were AMD cards, and they were getting hot very easily (the 7970 was going up to 100 degrees Celsius, I suspect that the fitting of the radiators were poorly made). I set up a watercooling system for the whole miner. Although it was a very interesting experience, it came to an end rather quickly. since ASICs kicked in and rendered graphics cards useless.

At the time, I started a project for handwriting character recognition (more on this project later…), and I used one of those cards to apply some graphics transformations (Hough transform and such). But they also consume a lot of electricity. So, sorry AMD, but I switched to Nvidia (I don’t own any stocks of either of those companies… or any other company, by the way, but I do believe that AMD is seriously losing ground here), which also has some great support with tools to build Neural Networks like TensorFlow.

So I wanted to start with a simple game and a simple goal:

  • create a neural network framework from scratch, with NNs as generic as possible, including non linear functions, spikes, max and many operations, the ones that will be best will be selected by the genetic algorithm,
  • a genetic algorithm to make those NNs evolve,
  • all this should be able to run both on CPUs and GPUs (at least the most power-consuming parts), and I originally planed to learn some Cuda although I had prior OpenCl experience.

But everything had to start somewhere. And I didn’t want to tackle Chess or Go as my first GANN project. Anyway, the DeepMind guys have done that already!

So I had to aim for something simple for a start, and at the same time some game that I didn’t know well so that I wouldn’t be tempted to direct the algorithm and make “Stockfish for game XXX”.  On the same note, another strong criteria was also that it shouldn’t be a game that had been solved by mainstream software, like Chess, Checkers or Reversi/Othello. At the website littlegolem on which I play board games, there are some games that I don’t know well (yet) and also many games for which mainstream software is not really available. One of them is called “Einstein Würfelt Nicht” (which I will call EWN in the future) and is actually a dice game, played on a 5×5 board. When I started this project, I had never played that game but the rules seemed quite simple and I decided to have a go at it. This was of course the first attempt, the first prototype, basically some starting point, but certainly not the end point.

There are also two variants of the game on the site, which would be also nice to tackle. My initial goal there was to defeat human players and become a top player on the site. Note that this particular site is also very flexible with its players, and doesn’t ban anyone. If you don’t like playing against cheaters or robots, then that site is definitely not for you. But personally I learn a lot by playing with any kind of opponent, it doesn’t matter if I win or if I lose.

I will develop that story in the next articles, and I will also post some of the code soon on my gitlab account. However, I will not post the full code, don’t expect to be able to have a running bot that would play for you at EWN from there. But the generic code will be there and will be adaptable to other games as well.

The next part is here.

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