2 ms·
What's the point of training a nnet on outputs from the original 3 rules so it can effectively just relearn them?
by adammarples 7mo ago
What's the point of training a nnet on outputs from the original 3 rules so it can effectively just relearn them?
- kjshsh123 7mo agoKinda agree. Training the network with RL instead and penalizing collisions and rewarding collecting something like food would be interesting. As long as the birds can't change direction too quickly (e.g. output acceleration, not velocity) I'd guess you get flocking.
- daemonologist 7mo agoI agree that this would be a more interesting approach; I think you might need more incentives to create a flock though (aerodynamic benefits, predator protection, etc.)
- ecto 7mo agoAwesome idea!
- ecto 7mo agoWhat happens when you want to simulate millions of rules? What if they weren't noids?
- brcmthrowaway 7mo agoYeah, this is well presented slop.
- ecto 7mo agoThanks!
- jayGlow 7mo agobased on the article the noida approach has better performance and is able to run on a GPU while the Sterling implementation must run on a cpu.
- adammarples 7mo agoPotentially though there's nothing stopping someone from writing the original three rules using cuda