Poorly programmed, it doesn't learn from its mistakes, the games get stuck in a loop because the snake doesn't capture a piece but the piece remains and there's a gap, constantly moving the snake along the same path with negative scores in an infinite loop leaving an unaltered yin and yang ;) there's a repetitive pattern in these infinite games between the position of the gap and the piece
I noticed snake gets penalized for not getting to the apple early, is that what you really want? Snake is about how long it gets not about the balance between length and wall clock time
Really cool! But right as it was nearing 4,000, it seems to have corrupted itself and no longer got any scores above 0. Not sure if that's a code bug or a neural net issue.
Yes it just collapses eventually — never stabilizes.
The training process is flawed, I suspect it has to do with the fact that some weights blow up over time, you can see in “weights” tab.
But at around 4K avg score you should see it solve the env almost every time.
Just a demo :) optimized for speed over stability.
Reward structure: Step: -1 Dot: +100 Win: +1000
so ~4k is max theoretical score on 6x6.
I think I noticed it reach “end game.” The snake reaches a point where, if it gets any longer, it is out of squares and hits its own tail. So it finds the route through the squares that it can infinitely loop, never eats the ball, and score starts dropping and goes negative.
My average eventually made it to about 3900, and then stagnated between 3600-3900. I'm curious if this is universal behavior or not. I'm up to about 5k steps.
my training on a 10x10 just randomly broke. i got to like 3600 then the graph went flat, the viewer on the left just showed it constantly restarting the game, and the scores in the negative. my average is now -10.
I noticed that if you go from training to watch and then back, the training temporarily drop significantly in score.
trained and made a viz for the model and then made it displace text.
should probably do a proper write-up:https://x.com/i/status/2038367016969724259
avg500 -4.6 last 500 episodes
peak 3959.3 best window
roll/s 20.68 20-step avg
progress 4388 562749 episodes
But at around 4K avg score you should see it solve the env almost every time.
Just a demo :) optimized for speed over stability.
Reward structure: Step: -1 Dot: +100 Win: +1000 so ~4k is max theoretical score on 6x6.
Looks like this is for Linux and Windows, on NetBSD I get this issue :(
> WebGPU is not yet available in Release or late Beta builds.