This is an enhancement of the awesome taget-seeking AI demo.

• Install nodejs,

• npm install

• then node server.js

• You can now view the simulation at: localhost:3000/index.html

• or you can view automated training at localhost:3000/

• average convergence: 74 generations! :-)

Target-seeking AI

This repository shows how you can use Neataptic to succesfully teach neural networks to trace targets. You can see the genomes live in action here. These genomes have been trained for over 100 generations and are very effective. Visualisation done with P5.js. The next step would be adding collisions, to possibly reveal some interesting tactics.

Read an article on this repo here.

Settings

If you manage to optimize the settings, please perform either a pull request or create an issue here.

Settings (contained in js/main.js):

• WIDTH set the width of the playing field
• HEIGHT set the height of the playing field
• MAX_SPEED set the maximal multiplier speed a genome can have (smaller genomes move faster)
• START_X set the x-location from which each genome (and the target) starts
• START_Y set the y-location from which each genome (and the target) starts
• SCORE_RADIUS set the distance to the target from where genomes get assigned score
• PLAYER_AMOUNT set the amount of genomes that play on the field (population size)
• ITERATIONS set the amount of iterations/frames each generation is tested for
• START_HIDDEN_SIZE set the amount of hidden nodes each genome starts witch
• MUTATION_RATE set the mutation rate
• ELITISM set the amount of elitism

Most important setting:

• USE_TRAINED_POP setting this to false will start the evolution from scratch (USE THIS WHEN OPTIMIZING THE SETTINGS), setting this to true will use the pre-trained population

Default setting values

var WIDTH            = $('#field').width();
var HEIGHT           = 800;
var MAX_SPEED        = 5;
var START_X          = WIDTH/2;
var START_Y          = HEIGHT/2;
var SCORE_RADIUS     = 100;

// GA settings
var PLAYER_AMOUNT    = Math.round(2.3e-4 * WIDTH * HEIGHT);
var ITERATIONS       = 250;
var MUTATION_RATE    = 0.3;
var ELITISM          = Math.round(0.1 * PLAYER_AMOUNT);

// Trained population
var USE_TRAINED_POP = true;