急速賽車 Q-learning | JavaScript 程式專案

{"id":24466,"student_id":10,"content":"let road = createSprite(\"road.png\");\nroad.rotationStyle = 'fixed';\nlet border= createSprite(\"border.png\");\nborder.rotationStyle = 'fixed';\nlet car = createSprite(\"car.png\");\ncar.scale = 0.4;\ncar.direction = 180;\n\nlet offsetX = 1500;\nlet offsetY = 650;\n\ncar.reset = function () {\n    this.direction = 180;\n    // this.y = 30;\n    // this.x = 200;\n    road.x = 320 + offsetX;\n    road.y = 240 + offsetY;\n    border.x = 320 + offsetX;\n    border.y = 240 + offsetY;\n    this.miles = 0;\n    this.sensors.forEach(s =\u003e s.length = 70);\n}\n\ncar.reset = function () {\n    this.direction = 270;\n    this.y = 30;\n    this.x = 200;\n    this.miles = 0;\n    this.sensors.forEach(s =\u003e s.length = 30);\n}\n\nfunction sigmoid (x) {\n    return (Math.exp(x) - Math.exp(-x)) / (Math.exp(x) + Math.exp(-x));\n}\n\nfunction NN (inputs, weights) {\n    var w = weights;\n    var i = inputs;\n\n    var a = i[0]*w[0] +  i[1]*w[1] +  i[2]*w[2] +  i[3]*w[3] +  i[4]*w[4]\n    var b = i[0]*w[5] +  i[1]*w[6] +  i[2]*w[7] +  i[3]*w[8] +  i[4]*w[9]\n    var c = i[0]*w[10] + i[1]*w[11] + i[2]*w[12] + i[3]*w[13] + i[4]*w[14]\n    var d = i[0]*w[15] + i[1]*w[16] + i[2]*w[17] + i[3]*w[18] + i[4]*w[19]\n\n    a = sigmoid(a);\n    b = sigmoid(b);\n    c = sigmoid(c);\n    d = sigmoid(d);\n\n    var d = a*w[20] + b*w[21] + c*w[22] + d*w[23]\n    var e = a*w[24] + b*w[25] + c*w[26] + d*w[27]\n\n    d = sigmoid(d);\n    e = sigmoid(e);\n\n    return [d, e];\n}\n\nfunction sensor (car, direction) {\n    var dot = createSprite('dot.jpg');\n    dot.scale = 2;\n    dot.length = 0;\n    dot.forever(()=\u003e{\n        dot.moveTo(car);\n        dot.direction = car.direction + direction;\n        dot.stepForward(dot.length);\n        pen.color = 'red';\n        pen.drawLine(car.x, car.y, dot.x, dot.y);\n\n        if (dot.touched(border)) {\n            if (dot.length \u003e 0) dot.length--;\n        } else {\n            if (dot.length \u003c 200) dot.length++;\n        }\n    });\n    return dot;\n}\n\ncar.sensors = [\n    sensor(car, -45),\n    sensor(car, -22),\n    sensor(car, 0),\n    sensor(car, 22),\n    sensor(car, 45),\n]\n\ncar.getSensorsData = function () {\n    var inputs = [];\n    car.sensors.forEach(s =\u003e inputs.push(s.length));\n    return inputs;\n}\n\nfunction randomDNA () {\n    var dna = [];\n    for (var i=0; i\u003c28; i++) dna.push(Math.random()*0.2 - 0.1);\n    return dna;\n}\n\nfunction randomEq (num) {\n    var arr = [];\n    var sum = 0;\n    for (var i=0; i\u003cnum; i++) {\n        var rand = Math.random();\n        sum += rand;\n        arr[i] = rand;\n    }\n    for (var i=0; i\u003cnum; i++) {\n        arr[i] /= sum;\n    }\n    return arr;\n}\n\nfunction crossover (a, b) {\n    var newDNA = [];\n    newDNA.score = 0;\n    for (var i=0; i\u003ca.length; i++) {\n        newDNA[i] = Math.random() \u003e 0.5 ? a[i] : b[i];\n\n        if (Math.random() \u003c 0.01) {\n            newDNA[i] = Math.random()*0.2 - 0.1;\n        }\n    }\n    return newDNA;\n}\n\nfunction nextGeneration () {\n    populations.sort((a, b) =\u003e a.score - b.score);\n\n    bestScore = populations[populations.length - 1].score;\n\n    var newPops = [];\n\n    for (var i=0; i\u003c20; i++) {\n        var a = Math.floor(Math.sqrt(Math.random()*400));\n        var b = Math.floor(Math.sqrt(Math.random()*400));\n        newPops.push(crossover(populations[a], populations[b]));\n    }\n\n    populations = newPops;\n}\n\nvar count = 0;\nvar running = true;\nvar currentTrain = 0;\nvar populations = [];\nvar bestScore = 0;\nfor (var i=0; i\u003c20; i++) {\n    populations.push(randomDNA());\n}\n\ncar.reset();\ncar.brain = populations[0];\n\nforever(()=\u003e{\n    if (running) {\n\n        var inputs = car.getSensorsData();\n        var outputs = NN(inputs, car.brain);\n\n        if (outputs[0] \u003e 0) {\n            car.direction += 1.5;\n        }\n        if (outputs[1] \u003e 0) {\n            car.direction -= 1.5;\n        }\n        car.stepForward(1);\n        car.miles++;\n\n        if (car.touched(border)) {\n            running = false;\n        }\n\n        print(bestScore, 10, 400);\n        print(count + '/' + currentTrain, 10, 420);\n        print(inputs, 10, 440);\n        print(outputs, 10, 460);\n\n    } else {\n        populations[currentTrain].score = car.miles;\n        currentTrain++;\n\n        if (currentTrain \u003c populations.length) {\n            car.brain = populations[currentTrain];\n            car.reset();  \n        } else {\n            nextGeneration();\n            currentTrain = 0;\n            count++;\n        }\n        running = true;\n    }\n});","created_at":"2018-03-14T15:54:51.948+08:00","updated_at":"2019-11-11T13:46:40.165+08:00","name":"急速賽車 Q-learning","language":"javascript","screenshot":{"url":"https://cdn6.koding.school/uploads/project/screenshot/24466/0999389cfcc4e0fa6c4ae33cdc279084.jpg"},"parent_id":9144,"plugin":"","description":null,"note":null,"status":"public","like_student_ids":[],"is_featured":false,"views":338,"hashid":"2pdsrzq9","is_content_changed":false,"review_status":"unsubmitted","submitted_at":null,"reviewed_at":null,"advise":null,"is_deleted":false}