TinyBrains - Competition for tiny neural networks that play strategy games
Show HN: A competition for small neural networks that play strategy games
TinyBrains challenges you to build the smallest neural network that plays well. Based on the classic Ants game from Google AI Challenge 2011, you train a network, describe it in a manifest, and submit both. Models are measured into weight classes based on total bytes—from 16 KiB nano to 64 MiB large. The goal isn't the strongest player, but the strongest play packed into the fewest bytes. Compete on the leaderboard, watch matches, and see how your tiny brain stacks up.
The contest is not who can build the strongest player, but who can pack the strongest play into the fewest bytes.
- codetiger
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.
- lostdog
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"
- adityamishra241
This looks fun. How small are the networks you're aiming for?