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.
Unfortunately the competition site is mostly down and couldn't find much about the old competition other than the participants blog articles. Do a search on "Google Ants AI Challenge - post mortem", and you get a lot of articles around the game.
Thanks for sharing the research. I tried implementing a per Ant decision making model, but gave up as the training time was much longer compared to the current baseline. I think I should rethink the idea.
Nice. I was 72nd. Working in AI research today and still making ai for games as a hobby (tfmbot.com is an ai i’m working on for my favourite board game terraforming mars).
From competing with the world to building a place for the world to compete—what a full-circle moment. Love the challenge: how much strategy can a tiny neural network learn? Excited to see what people build.
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?
Glad you like it. The evaluation is based purely on game performance. However each weight class is evaluated separately. Nano, micro, mini, small, large and open class.
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.
Where do you see a hard requirement? I have added support for ONNX model upload for now and would love to extend support for other formats. How you build the model is totally upto you. I don’t check anything other than format and inference time and model size.
"Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks"
https://arxiv.org/pdf/2502.13918
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.
Can you give more background on the Ants game?
I didn't find it on the current site or the older one.
Was the game inspired by anything like agent-based simulations?
I'm not super interested in what the tech industry is calling "agentic" AI, but I am interested in collective intelligence, see David Ha's work in this area: - https://journals.sagepub.com/doi/full/10.1177/26339137221114... - https://neurips.cc/virtual/2024/105817
Would be cool if each ant itself could be an agent
Thanks for sharing the research. I tried implementing a per Ant decision making model, but gave up as the training time was much longer compared to the current baseline. I think I should rethink the idea.
> 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
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
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"
https://en.wikipedia.org/wiki/Core_War