21 |
22 |
23 |
Playing field
24 |
25 | The following neural agents are a result of 100+ generations of evolution.
26 | If you have any remarks, or have managed to improve the evolution process,
27 | please make a post
28 | here. You can also perform a pull request on this repo. The genomes
29 | are alive live at the moment (every 250 frames). Click on the field
30 | to relocate the target!
31 |
32 |
33 |
34 |
Best genome
35 |
36 |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
52 |
53 |
54 |
55 |
--------------------------------------------------------------------------------
/js/field.js:
--------------------------------------------------------------------------------
1 | /* Global vars */
2 | var players = [];
3 | var walker = new Walker();
4 | var iteration = 0;
5 | var highestScore = 0;
6 |
7 | /** Setup the canvas */
8 | function setup(){
9 | var canvas = createCanvas(WIDTH, HEIGHT);
10 | canvas.parent('field');
11 | initNeat();
12 |
13 | // Do some initial mutation
14 | if(!USE_TRAINED_POP){
15 | for(var i = 0; i < 1; i++) neat.mutate();
16 | }
17 |
18 | startEvaluation();
19 | }
20 |
21 | function draw(){
22 | clear();
23 | squareGrid();
24 |
25 | // Check if evaluation is done
26 | if(iteration == ITERATIONS){
27 | endEvaluation();
28 | iteration = 0;
29 | }
30 |
31 | // Update and visualise players
32 | for(var i = players.length - 1; i >= 0; i--){
33 | var player = players[i];
34 |
35 | // Some players are eaten during the iteration
36 | player.update();
37 | player.show();
38 | }
39 |
40 | walker.update();
41 | walker.show();
42 |
43 | iteration++;
44 | }
45 |
46 | /** Draw a square grid with grey lines */
47 | function squareGrid(){
48 | stroke(204, 204, 204, 160);
49 | strokeWeight(1);
50 | fill(255);
51 | for(var x = 0; x < WIDTH/40; x++){
52 | line(x * 40, 0, x * 40, HEIGHT);
53 | }
54 | for(var y = 0; y < HEIGHT/40; y++){
55 | line(0, y * 40, WIDTH, y * 40);
56 | }
57 | noStroke();
58 | }
59 |
60 | /** Calculate distance between two points */
61 | function distance(x1, y1, x2, y2){
62 | var dx = x1 - x2;
63 | var dy = y1 - y2;
64 |
65 | return Math.sqrt(dx * dx + dy * dy);
66 | }
67 |
68 | /** Get a relative color between red and green */
69 | var activationColor = function(value, max){
70 | var power = 1 - Math.min(value/max, 1);
71 | var color = [255, 255, 0]
72 |
73 | if(power < 0.5){
74 | color[0] = 2 * power * 255;
75 | } else {
76 | color[1] = (1.0 - 2 * (power - 0.5)) * 255;
77 | }
78 |
79 | return color;
80 | }
81 |
82 | /** Get the angle from one point to another */
83 | function angleToPoint(x1, y1, x2, y2){
84 | d = distance(x1, y1, x2, y2);
85 | dx = (x2-x1) / d;
86 | dy = (y2-y1) / d;
87 |
88 | a = Math.acos(dx);
89 | a = dy < 0 ? 2 * Math.PI - a : a;
90 | return a;
91 | }
92 |
93 | /** Set the walker to a new location */
94 | function mouseClicked(){
95 | if(mouseX >= 0 && mouseX <= WIDTH && mouseY >= 0 && mouseY <= HEIGHT){
96 | walker.x = mouseX;
97 | walker.y = mouseY;
98 | }
99 | }
100 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Target-seeking AI
2 | This repository shows how you can use [Neataptic](https://github.com/wagenaartje/neataptic) to succesfully teach neural networks to trace targets. You can see the genomes live in action [here](https://wagenaartje.github.io/target-seeking-ai/). These genomes have been trained for over 100 generations and are very effective. Visualisation done with [P5.js](https://p5js.org/). The next step would be adding collisions, to possibly reveal some interesting tactics.
3 |
4 | [Read an article on this repo here](https://wagenaartje.github.io/neataptic/articles/targetseeking/).
5 |
6 | These forks of this library are interesting to check out as well:
7 |
8 | * [corpr8's fork](https://corpr8.github.io/neataptic-targetseeking-tron/)
9 | gives each neural agent its own acceleration, as well as letting each arrow
10 | remain in the same place after each generation. This creates a much more
11 | 'fluid' process.
12 |
13 | ## Settings
14 | If you manage to optimize the settings, please perform either a pull request or create an issue [here](https://github.com/wagenaartje/neataptic/issues).
15 |
16 | #### Settings (contained in `js/main.js`):
17 | * `WIDTH` set the width of the playing field
18 | * `HEIGHT` set the height of the playing field
19 | * `MAX_SPEED` set the maximal multiplier speed a genome can have (smaller genomes move faster)
20 | * `START_X` set the x-location from which each genome (and the target) starts
21 | * `START_Y` set the y-location from which each genome (and the target) starts
22 | * `SCORE_RADIUS` set the distance to the target from where genomes get assigned score
23 | * `PLAYER_AMOUNT` set the amount of genomes that play on the field (population size)
24 | * `ITERATIONS` set the amount of iterations/frames each generation is tested for
25 | * `START_HIDDEN_SIZE` set the amount of hidden nodes each genome starts witch
26 | * `MUTATION_RATE` set the mutation rate
27 | * `ELITISM` set the amount of elitism
28 |
29 | Most important setting:
30 | * `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
31 |
32 | #### Default setting values
33 | ```javascript
34 | var WIDTH = $('#field').width();
35 | var HEIGHT = 800;
36 | var MAX_SPEED = 5;
37 | var START_X = WIDTH/2;
38 | var START_Y = HEIGHT/2;
39 | var SCORE_RADIUS = 100;
40 |
41 | // GA settings
42 | var PLAYER_AMOUNT = Math.round(2.3e-4 * WIDTH * HEIGHT);
43 | var ITERATIONS = 250;
44 | var MUTATION_RATE = 0.3;
45 | var ELITISM = Math.round(0.1 * PLAYER_AMOUNT);
46 |
47 | // Trained population
48 | var USE_TRAINED_POP = true;
49 | ```
50 |
--------------------------------------------------------------------------------
/js/player.js:
--------------------------------------------------------------------------------
1 | function Player(genome){
2 | this.x = START_X;
3 | this.y = START_Y;
4 | this.vx = 0;
5 | this.vy = 0;
6 | this.r = 6;
7 |
8 | this.brain = genome;
9 | this.brain.score = 0;
10 |
11 | players.push(this);
12 | }
13 |
14 | Player.prototype = {
15 | /** Update the stats */
16 | update: function(){
17 | var input = this.detect();
18 | var output = this.brain.activate(input);
19 |
20 | var moveangle = output[0] * 2 * PI;
21 |
22 | // Calculate next position
23 | this.ax = Math.cos(moveangle);
24 | this.ay = Math.sin(moveangle);
25 | this.vx += this.ax;
26 | this.vy += this.ay;
27 |
28 | // Limit speeds to maximum speed
29 | this.vx = this.vx > MAX_SPEED ? MAX_SPEED : this.vx < -MAX_SPEED ? -MAX_SPEED : this.vx;
30 | this.vy = this.vy > MAX_SPEED ? MAX_SPEED : this.vy < -MAX_SPEED ? -MAX_SPEED : this.vy;
31 |
32 | this.x += this.vx;
33 | this.y += this.vy;
34 |
35 | // Limit position to width and height
36 | this.x = this.x >= WIDTH ? WIDTH : this.x <= 0 ? 0 : this.x;
37 | this.y = this.y >= HEIGHT ? HEIGHT : this.y <= 0 ? 0 : this.y;
38 |
39 | if(this.x == 0 || this.x == WIDTH) this.vx = -this.vx;
40 | if(this.y == 0 || this.y == HEIGHT) this.vy = -this.vy;
41 |
42 | this.score();
43 | },
44 |
45 | /** Calculate fitness of this players genome **/
46 | score: function(){
47 | var dist = distance(this.x, this.y, walker.x, walker.y);
48 | if(!isNaN(dist) && dist < SCORE_RADIUS){
49 | this.brain.score += SCORE_RADIUS - dist;
50 | }
51 |
52 | // Replace highest score to visualise
53 | highestScore = this.brain.score > highestScore ? this.brain.score : highestScore;
54 | },
55 |
56 | /** Display the player on the field, parts borrowed from the CodingTrain */
57 | show: function(){
58 | // Draw a triangle rotated in the direction of velocity
59 | var angle = angleToPoint(this.x, this.y, this.x + this.vx, this.y + this.vy) + HALF_PI;
60 | var color = activationColor(this.brain.score, highestScore);
61 |
62 | push();
63 | translate(this.x, this.y);
64 | rotate(angle);
65 |
66 | fill(color);
67 | beginShape();
68 | vertex(0, -this.r * 2);
69 | vertex(-this.r, this.r * 2);
70 | vertex(this.r, this.r * 2);
71 | endShape(CLOSE);
72 |
73 | pop();
74 | },
75 |
76 | /** Detect and normalize inputs */
77 | detect: function(){
78 | var dist = Math.sqrt(this.x, this.y, walker.x, walker.y) / Math.sqrt(WIDTH**2 + HEIGHT**2);
79 | var targetAngle = angleToPoint(this.x, this.y, walker.x, walker.y) / TWO_PI;
80 | var vx = (this.vx + MAX_SPEED) / MAX_SPEED;
81 | var vy = (this.vy + MAX_SPEED) / MAX_SPEED;
82 | var tvx = (walker.vx + MAX_SPEED) / MAX_SPEED;
83 | var tvy = (walker.vy + MAX_SPEED) / MAX_SPEED;
84 |
85 | // NaN checking
86 | targetAngle = isNaN(targetAngle) ? 0 : targetAngle;
87 | dist = isNaN(dist) ? 0 : dist;
88 |
89 | return [vx, vy, tvx, tvy, targetAngle, dist];
90 | },
91 | };
92 |
--------------------------------------------------------------------------------
/js/main.js:
--------------------------------------------------------------------------------
1 | /** Rename vars */
2 | var Neat = neataptic.Neat;
3 | var Methods = neataptic.Methods;
4 | var Config = neataptic.Config;
5 | var Architect = neataptic.Architect;
6 |
7 | /** Turn off warnings */
8 | Config.warnings = false;
9 |
10 | /** Settings */
11 | var WIDTH = $('#field').width();
12 | var HEIGHT = 800;
13 | var MAX_SPEED = 5;
14 | var START_X = WIDTH/2;
15 | var START_Y = HEIGHT/2;
16 | var SCORE_RADIUS = 100;
17 |
18 | // GA settings
19 | var PLAYER_AMOUNT = Math.round(2.3e-4 * WIDTH * HEIGHT);
20 | var ITERATIONS = 250;
21 | var MUTATION_RATE = 0.3;
22 | var ELITISM = Math.round(0.1 * PLAYER_AMOUNT);
23 |
24 | // Trained population
25 | var USE_TRAINED_POP = true;
26 |
27 | /** Global vars */
28 | var neat;
29 |
30 | /** Construct the genetic algorithm */
31 | function initNeat(){
32 | neat = new Neat(
33 | 6, 1,
34 | null,
35 | {
36 | mutation: [
37 | Methods.Mutation.ADD_NODE,
38 | Methods.Mutation.SUB_NODE,
39 | Methods.Mutation.ADD_CONN,
40 | Methods.Mutation.SUB_CONN,
41 | Methods.Mutation.MOD_WEIGHT,
42 | Methods.Mutation.MOD_BIAS,
43 | Methods.Mutation.MOD_ACTIVATION,
44 | Methods.Mutation.ADD_GATE,
45 | Methods.Mutation.SUB_GATE,
46 | Methods.Mutation.ADD_SELF_CONN,
47 | Methods.Mutation.SUB_SELF_CONN,
48 | Methods.Mutation.ADD_BACK_CONN,
49 | Methods.Mutation.SUB_BACK_CONN
50 | ],
51 | popsize: PLAYER_AMOUNT,
52 | mutationRate: MUTATION_RATE,
53 | elitism: ELITISM
54 | }
55 | );
56 |
57 | if(USE_TRAINED_POP){
58 | neat.population = population;
59 | }
60 |
61 | // Draw the first graph
62 | drawGraph(neat.population[0].graph($('.best').width()/2, $('.best').height()/2), '.best');
63 | }
64 |
65 | /** Start the evaluation of the current generation */
66 | function startEvaluation(){
67 | players = [];
68 | highestScore = 0;
69 |
70 | for(var genome in neat.population){
71 | genome = neat.population[genome];
72 | new Player(genome);
73 | }
74 |
75 | walker.reset();
76 | }
77 |
78 | /** End the evaluation of the current generation */
79 | function endEvaluation(){
80 | console.log('Generation:', neat.generation, '- average score:', Math.round(neat.getAverage()));
81 | console.log('Fittest score:', Math.round(neat.getFittest().score));
82 |
83 | // Networks shouldn't get too big
84 | for(var genome in neat.population){
85 | genome = neat.population[genome];
86 | genome.score -= genome.nodes.length * SCORE_RADIUS / 10;
87 | }
88 |
89 | // Sort the population by score
90 | neat.sort();
91 |
92 | // Draw the best genome
93 | drawGraph(neat.population[0].graph($('.best').width()/2, $('.best').height()/2), '.best');
94 |
95 | // Init new pop
96 | var newPopulation = [];
97 |
98 | // Elitism
99 | for(var i = 0; i < neat.elitism; i++){
100 | newPopulation.push(neat.population[i]);
101 | }
102 |
103 | // Breed the next individuals
104 | for(var i = 0; i < neat.popsize - neat.elitism; i++){
105 | newPopulation.push(neat.getOffspring());
106 | }
107 |
108 | // Replace the old population with the new population
109 | neat.population = newPopulation;
110 | neat.mutate();
111 |
112 | neat.generation++;
113 | startEvaluation();
114 | }
115 |
--------------------------------------------------------------------------------
/js/graph.js:
--------------------------------------------------------------------------------
1 | var NODE_RADIUS = 7;
2 | var REPEL_FORCE = 0;
3 | var LINK_DISTANCE = 100;
4 |
5 | var drawGraph = function(graph, panel) {
6 | var d3cola = cola.d3adaptor()
7 | .avoidOverlaps(true)
8 | .size([$('.best').width(), $('.best').height()]);
9 |
10 | var svg = d3.select(panel);
11 |
12 | d3.selectAll(panel + "> *").remove();
13 |
14 | // define arrow markers for graph links
15 | svg.append('svg:defs').append('svg:marker')
16 | .attr('id', 'end-arrow')
17 | .attr('viewBox', '0 -5 10 10')
18 | .attr('refX', 6)
19 | .attr('markerWidth', 4)
20 | .attr('markerHeight', 4)
21 | .attr('orient', 'auto')
22 | .append('svg:path')
23 | .attr('d', 'M0,-5L10,0L0,5')
24 |
25 | graph.nodes.forEach(function (v) { v.height = v.width = 2 * NODE_RADIUS; });
26 |
27 | d3cola
28 | .nodes(graph.nodes)
29 | .links(graph.links)
30 | .constraints(graph.constraints)
31 | .symmetricDiffLinkLengths(REPEL_FORCE)
32 | .linkDistance(LINK_DISTANCE)
33 | .start(10, 15, 20);
34 |
35 | var path = svg.selectAll(".link")
36 | .data(graph.links)
37 | .enter().append('svg:path')
38 | .attr('class', 'link')
39 |
40 | path.append("title")
41 | .text(function (d) {
42 | var text = "";
43 | text += "Weight: " + Math.round(d.weight*1000)/1000 + "\n";;
44 | text += "Source: " + d.source.id + "\n";;
45 | text += "Target: " + d.target.id;
46 | return text;
47 | });
48 |
49 | var node = svg.selectAll(".node")
50 | .data(graph.nodes)
51 | .enter().append("circle")
52 | .attr("class", function(d){
53 | return "node " + d.name;
54 | })
55 | .attr("r", function(d) { return NODE_RADIUS; })
56 |
57 | .call(d3cola.drag);
58 |
59 | node.append("title")
60 | .text(function (d){
61 | var text = "";
62 | text += "Activation: " + Math.round(d.activation*1000)/1000 + "\n";
63 | text += "Position: " + d.id;
64 | return text;
65 | });
66 |
67 | var label = svg.selectAll(".label")
68 | .data(graph.nodes)
69 | .enter().append("text")
70 | .attr("class", "label")
71 | .text(function (d){return '(' + d.index + ') ' + d.name; })
72 | .call(d3cola.drag)
73 |
74 | d3cola.on("tick", function () {
75 | // draw directed edges with proper padding from node centers
76 | path.attr('d', function (d) {
77 | var deltaX = d.target.x - d.source.x,
78 | deltaY = d.target.y - d.source.y,
79 | dist = Math.sqrt(deltaX * deltaX + deltaY * deltaY),
80 | normX = deltaX / dist,
81 | normY = deltaY / dist;
82 |
83 | if(isNaN(normX)) normX = 0;
84 | if(isNaN(normY)) normY = 0;
85 |
86 | sourcePadding = NODE_RADIUS,
87 | targetPadding = NODE_RADIUS + 2,
88 | sourceX = d.source.x + (sourcePadding * normX),
89 | sourceY = d.source.y + (sourcePadding * normY),
90 | targetX = d.target.x - (targetPadding * normX),
91 | targetY = d.target.y - (targetPadding * normY);
92 |
93 | // Defaults for normal edge.
94 | drx = 0,
95 | dry = 0,
96 | xRotation = 0, // degrees
97 | largeArc = 0, // 1 or 0
98 | sweep = 1; // 1 or 0
99 |
100 | // Self edge.
101 | if (d.source.x === d.target.x && d.source.y === d.target.y) {
102 | drx = dist;
103 | dry = dist;
104 | xRotation = -45;
105 | largeArc = 1;
106 | drx = 20;
107 | dry = 20;
108 | targetX = targetX + 1;
109 | targetY = targetY + 1;
110 | }
111 | return 'M' + sourceX + ',' + sourceY + "A" + drx + "," + dry + " " + xRotation + "," + largeArc + "," + sweep + " " + targetX + ',' + targetY;
112 | });
113 |
114 | node.attr("cx", function (d) { return d.x; })
115 | .attr("cy", function (d) { return d.y; })
116 |
117 | label
118 | .attr("x", function (d) { return d.x + 10; })
119 | .attr("y", function (d) { return d.y - 10; });
120 | });
121 | };
122 |
--------------------------------------------------------------------------------
/libs/webcola.js:
--------------------------------------------------------------------------------
1 | var cola;!function(a){var b=function(){function a(){this.locks={}}return a.prototype.add=function(a,b){isNaN(b[0])||isNaN(b[1]),this.locks[a]=b},a.prototype.clear=function(){this.locks={}},a.prototype.isEmpty=function(){for(var a in this.locks)return!1;return!0},a.prototype.apply=function(a){for(var b in this.locks)a(b,this.locks[b])},a}();a.Locks=b;var c=function(){function a(a,c,e){void 0===e&&(e=null),this.D=c,this.G=e,this.threshold=1e-4,this.random=new d,this.project=null,this.x=a,this.k=a.length;var f=this.n=a[0].length;this.H=new Array(this.k),this.g=new Array(this.k),this.Hd=new Array(this.k),this.a=new Array(this.k),this.b=new Array(this.k),this.c=new Array(this.k),this.d=new Array(this.k),this.e=new Array(this.k),this.ia=new Array(this.k),this.ib=new Array(this.k),this.xtmp=new Array(this.k),this.locks=new b,this.minD=Number.MAX_VALUE;for(var g,h=f;h--;)for(g=f;--g>h;){var i=c[h][g];i>0&&i
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c.length>0?c[0]:null},a.prototype.vertices=function(){return[{x:this.x,y:this.y},{x:this.X,y:this.y},{x:this.X,y:this.Y},{x:this.x,y:this.Y},{x:this.x,y:this.y}]},a.lineIntersection=function(a,b,c,d,e,f,g,h){var i=c-a,j=g-e,k=d-b,l=h-f,m=l*i-j*k;if(0==m)return null;var n=a-e,o=b-f,p=j*o-l*n,q=p/m,r=i*o-k*n,s=r/m;return q>=0&&1>=q&&s>=0&&1>=s?{x:a+q*i,y:b+q*k}:null},a.prototype.inflate=function(b){return new a(this.x-b,this.X+b,this.y-b,this.Y+b)},a}();a.Rectangle=p,a.makeEdgeBetween=c,a.makeEdgeTo=d;var q=function(){function a(a,b,c){this.v=a,this.r=b,this.pos=c,this.prev=f(),this.next=f()}return a}(),r=function(){function a(a,b,c){this.isOpen=a,this.v=b,this.pos=c}return a}(),s={getCentre:function(a){return a.cx()},getOpen:function(a){return a.y},getClose:function(a){return a.Y},getSize:function(a){return a.width()},makeRect:function(a,b,c,d){return new p(c-d/2,c+d/2,a,b)},findNeighbours:i},t={getCentre:function(a){return a.cy()},getOpen:function(a){return 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