adaptive training #2

run length:48
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
avg 0.11333 0.1216922 0.1410408 0.2255169 0.3438947 0.5308622 0.7092866 0.7473413 0.8380993 0.8315367 0.9242428 0.8393147 1.103198 1.054069 1.111683 1.051629 1.028054 1.043636 0.9677835 1.01511 1.045051 1.138088 1.002189 0.8751988 0.9423944 0.9026732 1.062976 0.9172046 1.334726 1.079931 0.7032812 1.377699 1.236436 1.741362 1.081763 1.341366 0.8942567 0.9266959 1.079842 1.067705 0.7557345 1.354618 0.9338629 1.070021 1.288732 1.196118 1.037447
max 0.4842224 0.7318087 0.7872303 1.314361 1.167771 1.026197 1.422564 1.538746 1.474501 1.283453 1.423418 1.349116 1.614929 1.392465 1.4685 1.274314 1.203464 1.228827 1.08125 1.171278 1.106939 1.359823 1.106474 0.9218987 1.06358 1.007161 1.376667 0.9957669 1.447871 1.173347 0.8280649 1.515407 1.369468 1.839345 1.161362 1.377432 0.9441369 0.9500773 1.109659 1.092207 0.7708632 1.38819 0.9459927 1.093874 1.322104 1.24041 1.065003
Population Size
100
Run Length
20
Do Training
False
Do Hybrid Training
True
Do Competitive Run
False
Do Adaptive Training
True
Inputs
hasTarget,targetLeft,targetRight,targetNorth,targetSouth,dirToTarget,distToTarget,wall,sensor0,sensor1,sensor2
Outputs
left,right,up,down,run,sensordir0,sensordir1,sensordir2
Hidden Layers
8 12 12 12 8
Back Propogation
True
Learning rate
0.5
Momentum
0.1
Growth rate
0.5