Implemented Reinforcement Learning to train the model(car) to follow the path with no obstacles
While training the models the data get stored in the localstorage of the browser on click of the save button
With mutiple times training the model, the optimum value of the weight, bias, levels etc was
{"levels":
[{"inputs":[0.6685330400215612,0.38752891994436545,0,0,0],"outputs":[1,0,0,0,1,1],
"biases":[0.08722929461966775,0.09352942586808946,-0.14441443709413293,0.01946514167299861,-0.32132693964273756,-0.016592452161308932],
"weights":[[0.3042758280911088,-0.29640352591708446,-0.24314082816433147,0.02806620611642703,-0.15507298049487217,0.026567303699061684],
[0.09122771131968085,0.008219035617384698,-0.14113001781840753,-0.2918547779225645,0.15463968840385092,-0.08057791579498447],
[0.10328998303182277,0.29382687832393883,0.3397243527355235,-0.13937470529538432,-0.07580474919393697,-0.12275791454795776],
[-0.03231829398308536,0.28178945116296356,-0.2204228572063498,0.1762562892069908,-0.1753132788956984,-0.03766182072223565],
[-0.2575686158705198,0.09606022774771153,0.1676324732603136,0.21642869315401303,0.06494210441584784,-0.08475996922990445]]},
{"inputs":[1,0,0,0,1,1],"outputs":[1,0,0,0],
"biases":[-0.07378515380860413,-0.17011519553048426,0.16523169341178282,0.07317421821354596],
"weights":[[-0.016478546363272076,-0.16265142183164244,-0.002195614379118499,-0.024079981474259438],[-0.25234399369718474,0.03600079280648957,-0.32231580862183357,0.13772100280237543],
[0.058566530287304505,-0.20729777656922,0.3020946892407153,0.11326236441712263],
[0.13331865877379923,0.21380795850182543,-0.05455272285121646,-0.15252517003133298],
[0.13119599052496214,-0.02532355860209006,0.008930207734655197,-0.24038829111972582],
[0.1455607695638779,-0.24065409444568356,0.1481754674997934,-0.130410504823829]]}]}
working model : https://anikkk.github.io/self-driving-car/