15yrs back I participated in "Google Ants AI Challenge 2011", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The competition gave me a huge learning oppurtunity where developers across the world came to a forum and discussed various techniques.

Now, I've built a similar platform to bring back the fun of building a small neural network that can play the game well. Neural Network optimization seems to be much more fun.

Plz share your feedback to improve the platform and add more games.

Let's play real stuff.

"Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks"

https://arxiv.org/pdf/2502.13918

OMG!

Just yesterday I published my reworked GoNEAT library that implements HyperNEAT combined with phased search and backpropagation [1].

But it's kind of impossible to enter for me because of the hard pytorch requirements :( would love to see the project as a gym, so that you can run your own ANN design algorithm.

I get that most data science students still use python, but the evolutionary world is kinda in C++ and other native languages.

Anyways, great project nonetheless.

[1] https://github.com/cookiengineer/goneat

Around 1997 there was something awfully similar to this doing the rounds, called AI Wars. IIRC it was code rather than weights, but it's a funny parallel. 30 years! God I'm old.

Interesting—how small can a neural network get and still make good strategic decisions? Curious whether these models can adapt to unfamiliar opponents.