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Learner.js
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Learner.js
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var synaptic = require('synaptic');
var async = require('async');
var _ = require('lodash');
var Architect = synaptic.Architect;
var Network = synaptic.Network;
var Learn = {
// Array of networks for current Genomes
// (Genomes will be added the key `fitness`)
genomes: [],
// Current state of learning [STOP, LEARNING]
state: 'STOP',
// Current genome/generation tryout
genome: 0,
generation: 0,
// Set this, to verify genome experience BEFORE running it
shouldCheckExperience: false,
};
// Initialize the Learner
Learn.init = function (gameManip, ui, genomeUnits, selection, mutationProb) {
Learn.gm = gameManip;
Learn.ui = ui;
Learn.genome = 0;
Learn.generation = 0;
Learn.genomeUnits = genomeUnits;
Learn.selection = selection;
Learn.mutationProb = mutationProb;
}
// Build genomes before calling executeGeneration.
Learn.startLearning = function () {
// Build genomes if needed
while (Learn.genomes.length < Learn.genomeUnits) {
Learn.genomes.push(Learn.buildGenome(3, 1));
}
Learn.executeGeneration();
}
// Given the entire generation of genomes (An array),
// applyes method `executeGenome` for each element.
// After all elements have completed executing:
//
// 1) Select best genomes
// 2) Does cross over (except for 2 genomes)
// 3) Does Mutation-only on remaining genomes
// 4) Execute generation (recursivelly)
Learn.executeGeneration = function (){
if (Learn.state == 'STOP') {
return;
}
Learn.generation++;
Learn.ui.logger.log('Executing generation '+Learn.generation);
Learn.genome = 0;
async.mapSeries(Learn.genomes, Learn.executeGenome, function (argument) {
// Kill worst genomes
Learn.genomes = Learn.selectBestGenomes(Learn.selection);
// Copy best genomes
var bestGenomes = _.clone(Learn.genomes);
// Cross Over ()
while (Learn.genomes.length < Learn.genomeUnits - 2) {
// Get two random Genomes
var genA = _.sample(bestGenomes).toJSON();
var genB = _.sample(bestGenomes).toJSON();
// Cross over and Mutate
var newGenome = Learn.mutate(Learn.crossOver(genA, genB));
// Add to generation
Learn.genomes.push(Network.fromJSON(newGenome));
}
// Mutation-only
while (Learn.genomes.length < Learn.genomeUnits) {
// Get two random Genomes
var gen = _.sample(bestGenomes).toJSON();
// Cross over and Mutate
var newGenome = Learn.mutate(gen);
// Add to generation
Learn.genomes.push(Network.fromJSON(newGenome));
}
Learn.ui.logger.log('Completed generation '+Learn.generation);
// Execute next generation
Learn.executeGeneration();
})
}
// Sort all the genomes, and delete the worst one
// untill the genome list has selectN elements.
Learn.selectBestGenomes = function (selectN){
var selected = _.sortBy(Learn.genomes, 'fitness').reverse();
while (selected.length > selectN) {
selected.pop();
}
Learn.ui.logger.log('Fitness: '+_.pluck(selected, 'fitness').join(','));
return selected;
}
// Waits the game to end, and start a new one, then:
// 1) Set's listener for sensorData
// 2) On data read, applyes the neural network, and
// set it's output
// 3) When the game has ended and compute the fitness
Learn.executeGenome = function (genome, next){
if (Learn.state == 'STOP') {
return;
}
Learn.genome = Learn.genomes.indexOf(genome) + 1;
// Learn.ui.logger.log('Executing genome '+Learn.genome);
// Check if genome has AT LEAST some experience
if (Learn.shouldCheckExperience) {
if (!Learn.checkExperience(genome)) {
genome.fitness = 0;
// Learn.ui.logger.log('Genome '+Learn.genome+' has no min. experience');
return next();
}
}
Learn.gm.startNewGame(function (){
// Reads sensor data, and apply network
Learn.gm.onSensorData = function (){
var inputs = [
Learn.gm.sensors[0].value,
Learn.gm.sensors[0].size,
Learn.gm.sensors[0].speed,
];
// console.log(inputs);
// Apply to network
var outputs = genome.activate(inputs);
Learn.gm.setGameOutput(outputs[0]);
}
// Wait game end, and compute fitness
Learn.gm.onGameEnd = function (points){
Learn.ui.logger.log('Genome '+Learn.genome+' ended. Fitness: '+points);
// Save Genome fitness
genome.fitness = points;
// Go to next genome
next();
}
});
}
// Validate if any acction occur uppon a given input (in this case, distance).
// If genome only keeps a single activation value for any given input,
// it will return false
Learn.checkExperience = function (genome) {
var step = 0.1, start = 0.0, stop = 1;
// Inputs are default. We only want to test the first index
var inputs = [0.0, 0.3, 0.2];
var activation, state, outputs = {};
for (var k = start; k < stop; k += step) {
inputs[0] = k;
activation = genome.activate(inputs);
state = Learn.gm.getDiscreteState(activation);
outputs[state] = true;
}
// Count states, and return true if greater than 1
return _.keys(outputs).length > 1;
}
// Load genomes saved from JSON file
Learn.loadGenomes = function (genomes, deleteOthers){
if (deleteOthers) {
Learn.genomes = [];
}
var loaded = 0;
for (var k in genomes) {
Learn.genomes.push(Network.fromJSON(genomes[k]));
loaded++;
}
Learn.ui.logger.log('Loaded '+loaded+' genomes!');
}
// Builds a new genome based on the
// expected number of inputs and outputs
Learn.buildGenome = function (inputs, outputs) {
Learn.ui.logger.log('Build genome '+(Learn.genomes.length+1));
var network = new Architect.Perceptron(inputs, 4, 4, outputs);
return network;
}
// SPECIFIC to Neural Network.
// Those two methods convert from JSON to Array, and from Array to JSON
Learn.crossOver = function (netA, netB) {
// Swap (50% prob.)
if (Math.random() > 0.5) {
var tmp = netA;
netA = netB;
netB = tmp;
}
// Clone network
netA = _.cloneDeep(netA);
netB = _.cloneDeep(netB);
// Cross over data keys
Learn.crossOverDataKey(netA.neurons, netB.neurons, 'bias');
return netA;
}
// Does random mutations across all
// the biases and weights of the Networks
// (This must be done in the JSON to
// prevent modifying the current one)
Learn.mutate = function (net){
// Mutate
Learn.mutateDataKeys(net.neurons, 'bias', Learn.mutationProb);
Learn.mutateDataKeys(net.connections, 'weight', Learn.mutationProb);
return net;
}
// Given an Object A and an object B, both Arrays
// of Objects:
//
// 1) Select a cross over point (cutLocation)
// randomly (going from 0 to A.length)
// 2) Swap values from `key` one to another,
// starting by cutLocation
Learn.crossOverDataKey = function (a, b, key) {
var cutLocation = Math.round(a.length * Math.random());
var tmp;
for (var k = cutLocation; k < a.length; k++) {
// Swap
tmp = a[k][key];
a[k][key] = b[k][key];
b[k][key] = tmp;
}
}
// Given an Array of objects with key `key`,
// and also a `mutationRate`, randomly Mutate
// the value of each key, if random value is
// lower than mutationRate for each element.
Learn.mutateDataKeys = function (a, key, mutationRate){
for (var k = 0; k < a.length; k++) {
// Should mutate?
if (Math.random() > mutationRate) {
continue;
}
a[k][key] += a[k][key] * (Math.random() - 0.5) * 3 + (Math.random() - 0.5);
}
}
module.exports = Learn;