9 comments

  • DylanMerigaud 27 minutes ago
    Great idea to focus on small, efficient neural networks.
  • codetiger 16 hours ago
    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.

    • AnotherGoodName 6 hours ago
      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).
      • codetiger 5 hours ago
        Thanks for sharing. I remember #1 xathis had a score, big leap ahead of others. The difference in techniques in top 100 was almost the same.
    • atmanactive 12 hours ago
      I remember a game on Steam called Tiny Brains, great couch co-op.
  • adityamishra241 1 hour ago
    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?
  • willmarch 4 hours ago
    Pretty neat! I'm considering entering some models. How long will you be running these competitions?
    • codetiger 4 hours ago
      The current season is a public beta and ends by end of the month. After that am considering 3 month seasons.
  • Qworg 3 hours ago
    Reminds me of MechMania at UIUC - exciting!
  • cookiengineer 1 hour ago
    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

  • adityamishra241 14 hours ago
    This looks fun. How small are the networks you're aiming for?
    • sitzkrieg 8 hours ago
      the network size brackets are in TFA:

          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
    • codetiger 4 hours ago
      Each season has a different weight size restrictions. Currently open season is for a full production test.
  • lokar 6 hours ago
  • lostdog 6 hours ago
    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"

    • codetiger 5 hours ago
      Thanks for the feedback. I’ll take that as top priority.