Comments (2)
Hi!
In this model you can imagine birds as vehicles with two motors - kinda like:
... where r0
determines the speed of the "left motor" and r1
determines speed of the "right motor", making:
rotation = r0 - r1
, since you just rotate away from whichever side is moving faster,speed = r0 + r1
; although - now that I look at it - this one is probably kinda wrong (should be(r0 + r1) / 2
?), because if we imagine bothr0
andr1
going at 10 units per second, then in reality the entire bird would be moving at 10 units per second as well; fortunately, it shouldn't affect the computations much either way 😇
As for "but why" - if I recall correctly, modeling it this way has gotten me a bit better results (i.e. better-flying, faster-learning birds) than the simpler approach where you'd just extract speed & rotation directly from the network, like so:
crate fn propagate(&self, vision: Vec<f32>) -> (f32, f32) {
let response = self.nn.propagate(vision);
let speed = response[0].clamp(0.0, 1.0) - 0.5;
let rotation = response[1].clamp(0.0, 1.0) - 0.5;
(speed, rotation)
}
Lemme know if it makes sense now! 🙂
from shorelark.
Hi, to keep the issue tracker tidy, I'm going to close this issue - if you have any more questions, feel free to ask them here 🙂
from shorelark.
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