Show HN: A competition for small neural networks that play strategy games

This reminds me of John Conway's Game of Life. I wonder what surprises we could get by combining Game of Life with neural networks

Let's play real stuff.

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

https://arxiv.org/pdf/2502.13918

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.

Man, I remember doing this is 2011 as well. Everything some kind of hand-coded strategy. I enjoyed it a lot.

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

This looks fun. How do you evaluate the networks — is it purely based on game performance, or are there other metrics like size and inference speed too?

Pretty neat! I'm considering entering some models. How long will you be running these competitions?

Great idea to focus on small, efficient neural networks.

> Your class is measured, not chosen

> model and manifest bytes together pick the class

What?

How hard is it to write something like "your weight class is determined by the total size of the model and manifest" (if I understood it correctly).

Current version both sounds very AI-sloppy and is ambiguous.

The doc page [0] is even more painful to read.

[0] https://tinybrains.dev/docs/models/weight-classes.html

Reminds me of MechMania at UIUC - exciting!

This looks fun. How small are the networks you're aiming for?

    nano up to 16 KiB
    micro up to 128 KiB 
    mini up to 1 MiB 
    small up to 8 MiB 
    large up to 64 MiB

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

Cool idea!

It would help to delete all the text on the page, and write it without AI.

For example, "model and manifest bytes together pick the class; every version also plays on Open"

See also:

https://en.wikipedia.org/wiki/Core_War