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shiffmanshiffman
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one more neuroevolution example
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‎examples/NeuroEvolution-sensors/creature.js‎

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class Creature {
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constructor(x, y, brain) {
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this.position = createVector(x, y);
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this.velocity = createVector(0, 0);
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this.acceleration = createVector(0, 0);
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this.fullSize = 12;
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this.r = this.fullSize;
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this.maxspeed = 2;
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this.sensors = [];
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this.health = 100;
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let totalSensors = 15;
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for (let i = 0; i < totalSensors; i++) {
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let a = map(i, 0, totalSensors, 0, TWO_PI);
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let v = p5.Vector.fromAngle(a);
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v.mult(this.fullSize * 1.5);
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this.sensors[i] = new Sensor(v);
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}
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if (brain) {
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this.brain = brain;
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} else {
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this.brain = ml5.neuralNetwork({
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inputs: this.sensors.length,
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outputs: 2,
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task: "regression",
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neuroEvolution: true,
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});
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}
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}
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reproduce() {
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let brain = this.brain.copy();
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brain.mutate(0.1);
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return new Creature(this.position.x, this.position.y, brain);
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}
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eat() {
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for (let i = 0; i < food.length; i++) {
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let d = p5.Vector.dist(this.position, food[i].position);
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if (d < this.r + food[i].r) {
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this.health += 0.5;
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food[i].r -= 0.05;
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if (food[i].r < 20) {
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food[i] = new Food();
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}
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}
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}
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}
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think() {
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for (let i = 0; i < this.sensors.length; i++) {
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this.sensors[i].value = 0;
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for (let j = 0; j < food.length; j++) {
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this.sensors[i].sense(this.position, food[j]);
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}
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}
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let inputs = [];
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for (let i = 0; i < this.sensors.length; i++) {
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inputs[i] = this.sensors[i].value;
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}
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// Predicting the force to apply
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const outputs = this.brain.predictSync(inputs);
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let angle = outputs[0].value * TWO_PI;
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let magnitude = outputs[1].value;
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let force = p5.Vector.fromAngle(angle).setMag(magnitude);
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this.applyForce(force);
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}
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// Method to update location
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update() {
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// Update velocity
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this.velocity.add(this.acceleration);
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// Limit speed
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this.velocity.limit(this.maxspeed);
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this.position.add(this.velocity);
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// Reset acceleration to 0 each cycle
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this.acceleration.mult(0);
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this.health -= 0.25;
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}
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// Wraparound
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borders() {
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if (this.position.x < -this.r) this.position.x = width + this.r;
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if (this.position.y < -this.r) this.position.y = height + this.r;
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if (this.position.x > width + this.r) this.position.x = -this.r;
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if (this.position.y > height + this.r) this.position.y = -this.r;
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}
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applyForce(force) {
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// We could add mass here if we want A = F / M
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this.acceleration.add(force);
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}
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show() {
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push();
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translate(this.position.x, this.position.y);
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for (let sensor of this.sensors) {
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stroke(0, this.health * 2);
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line(0, 0, sensor.v.x, sensor.v.y);
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if (sensor.value > 0) {
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fill(255, sensor.value * 255);
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stroke(0, 100);
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circle(sensor.v.x, sensor.v.y, 4);
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}
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}
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noStroke();
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fill(0, this.health * 2);
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this.r = map(this.health, 0, 100, 2, this.fullSize);
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this.r = constrain(this.r, 2, this.fullSize);
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circle(0, 0, this.r * 2);
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pop();
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}
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}

‎examples/NeuroEvolution-sensors/food.js‎

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class Food {
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constructor() {
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this.position = createVector(random(width), random(height));
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this.r = 50;
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}
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show() {
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noStroke();
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fill(0, 100);
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circle(this.position.x, this.position.y, this.r * 2);
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}
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}

‎examples/NeuroEvolution-sensors/index.html‎

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta http-equiv="X-UA-Compatible" content="IE=edge" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>ml5.js NeuroEvolution Steering</title>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.6.0/p5.js"></script>
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<script src="../../dist/ml5.js"></script>
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</head>
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<body>
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<script src="sensor.js"></script>
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<script src="food.js"></script>
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<script src="vehicle.js"></script>
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<script src="sketch.js"></script>
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</body>
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</html>

‎examples/NeuroEvolution-sensors/sensor.js‎

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class Sensor {
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constructor(v) {
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this.v = v.copy();
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this.value = 0;
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}
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sense(position, food) {
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//{!1} Find the "tip" (or endpoint) of the sensor by adding position
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let end = p5.Vector.add(position, this.v);
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//{!1} How far is it from the food center
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let d = end.dist(food.position);
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//{!1} If it is within the radius light up the sensor
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if (d < food.r) {
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// The further into the center the food, the more the sensor activates
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this.value = 1;
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} else {
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// this.value = 0;
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}
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}
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}

‎examples/NeuroEvolution-sensors/sketch.js‎

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let bloops = [];
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let timeSlider;
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let food = [];
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function setup() {
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createCanvas(640, 240);
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ml5.tf.setBackend("cpu");
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for (let i = 0; i < 20; i++) {
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bloops[i] = new Creature(random(width), random(height));
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}
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for (let i = 0; i < 8; i++) {
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food[i] = new Food();
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}
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timeSlider = createSlider(1, 20, 1);
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}
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function draw() {
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background(255);
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for (let i = 0; i < timeSlider.value(); i++) {
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for (let i = bloops.length - 1; i >= 0; i--) {
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bloops[i].think();
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bloops[i].eat();
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bloops[i].update();
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bloops[i].borders();
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if (bloops[i].health < 0) {
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bloops.splice(i, 1);
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} else if (random(1) < 0.001) {
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let child = bloops[i].reproduce();
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bloops.push(child);
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}
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}
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}
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for (let treat of food) {
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treat.show();
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}
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for (let bloop of bloops) {
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bloop.show();
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}
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}

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