mirror of
https://github.com/wassname/rl_2d_walker.js.git
synced 2026-09-24 13:50:30 +08:00
249 lines
7.3 KiB
JavaScript
249 lines
7.3 KiB
JavaScript
var requestAnimFrame = window.requestAnimationFrame || window.webkitRequestAnimationFrame || window.mozRequestAnimationFrame || window.oRequestAnimationFrame || window.msRequestAnimationFrame || function (callback) { window.setTimeout(callback, 1000 / 60); };
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config = {
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time_step: 60,
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simulation_fps: 60,
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draw_fps: 60,
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velocity_iterations: 8,
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position_iterations: 3,
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max_zoom_factor: 130,
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min_motor_speed: -2,
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max_motor_speed: 2,
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population_size: 4,
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walker_health: 100,
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max_floor_tiles: 30,
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round_length: 18000,
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min_body_delta: 0,
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min_leg_delta: 0.0,
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};
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globals = {};
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chooseQoute = function () {
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var qoutes = [
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'Play the funky music, robot',
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'The origin of funkd',
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'The chaos computer club',
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'Only the humans that like to dance survived',
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'Classic robot dance move - the human',
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'First we dance Manhatten, then we dance the world',
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'Video of subjects one hour after ingesting substance q1043',
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'Red robot redemption',
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'Father was a rolling robot',
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'Light as a trash can, nimble as a ox',
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'Have you tried turning it off and on again?',
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'Eurovision 2050',
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]
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var qoute = qoutes[Math.randi(0,qoutes.length)]
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document.getElementById('page_quote').innerText = '"'+qoute+'"'
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}
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displayProgress = function () {
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// TODO show stats
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var stats = {
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'trainingTime': globals.step_counter / config.simulation_fps,
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'meanProgress': globals.walkers.map(w => w.last_position).reduce((s, v) => s + v) / globals.walkers.length,
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'meanReward': globals.walkers.map(w => w.reward).reduce((s, v) => s + v) / globals.walkers.length,
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'bufferSize': globals.agents[0].brain.buffer.size
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}
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document.getElementById('stats-prog').innerText = JSON.stringify(stats, null, 2)
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}
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gameInit = function() {
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var joints = 12
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var bodyParts = 14
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var state = bodyParts * 7
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var actions = joints
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var input = 2 * state + 1 * actions
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globals.brains = {
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actor: new window.neurojs.Network.Model([
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{ type: 'input', size: input },
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{ type: 'fc', size: 60, activation: 'relu' },
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{ type: 'noise', sigma: 0.3, delta: 0.1, theta: 0.15 },
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{ type: 'fc', size: 40, activation: 'relu' },
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{ type: 'noise', sigma: 0.3, delta: 0.1, theta: 0.15 },
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{ type: 'fc', size: 40, activation: 'relu' },
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{ type: 'fc', size: 40, activation: 'relu', dropout: 0.30 },
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// delta represents the equilibrium or mean value supported by fundamentals;
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// sigma the degree of volatility around it caused by shocks,
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// theta the rate by which these shocks dissipate and the variable reverts towards the mean.
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{ type: 'noise', sigma: 0.1, delta: 0.1, theta: 0.15 },
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{ type: 'fc', size: actions, activation: 'tanh' },
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{ type: 'regression' }
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]),
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critic: new window.neurojs.Network.Model([
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{ type: 'input', size: input + actions },
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{ type: 'fc', size: 80, activation: 'relu' },
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{ type: 'fc', size: 70, activation: 'relu' },
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{ type: 'fc', size: 60, activation: 'relu' },
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{ type: 'fc', size: 50, activation: 'relu' },
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{ type: 'fc', size: 1 },
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{ type: 'regression' }
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])
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}
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globals.brains.shared = new window.neurojs.Shared.ConfigPool()
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// this.brains.shared.set('actor', this.brains.actor.newConfiguration())
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globals.brains.shared.set('critic', globals.brains.critic.newConfiguration())
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chooseQoute()
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globals.world = new b2.World(new b2.Vec2(0, -10));
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[globals.agents, globals.walkers] = createPopulation();
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globals.floor = createFloor();
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drawInit();
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globals.step_counter = 0;
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// globals.simulation_interval = setInterval(simulationStep, Math.round(1000/config.simulation_fps));
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// globals.draw_interval = setInterval(drawFrame, Math.round(1000 / config.draw_fps));
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globals.display_interval = setInterval(displayProgress, Math.round(380 * 1000 / config.draw_fps));
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globals.charts_interval = setInterval(updateCharts, Math.round(380 * 1000 / config.draw_fps));
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globals.running = true
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requestAnimFrame(loop)
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}
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loop = function () {
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simulationStep()
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drawFrame()
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if (globals.running) requestAnimFrame(loop); // start next timer
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}
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resetSimulation = function () {
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// turn training off temporarlity to avoid NaN's
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updateIfLearning(false)
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console.log('resetting walkers')
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for(var k = 0; k < config.population_size; k++) {
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globals.agents[k].walker = globals.walkers[k] = new Walker(globals.world)
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}
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setTimeout(()=>updateIfLearning(true), 1000)
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}
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simulationStep = function () {
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globals.step_counter++;
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// step world
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globals.world.Step(1/config.time_step, config.velocity_iterations, config.position_iterations);
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globals.world.ClearForces();
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// step agents
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populationSimulationStep();
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}
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updateCharts = function () {
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if (globals.agents[0].infos.length) {
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if (!globals.charts) {
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globals.charts = new Charts()
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globals.charts.init(globals.agents)
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} else {
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globals.charts.update(globals.agents)
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}
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}
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}
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// setSimulationFps = function(fps) {
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// config.simulation_fps = fps;
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// clearInterval(globals.simulation_interval);
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// if(fps > 0) {
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// globals.simulation_interval = setInterval(simulationStep, Math.round(1000/config.simulation_fps));
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// if(globals.paused) {
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// globals.paused = false;
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// if(config.draw_fps > 0) {
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// globals.draw_interval = setInterval(drawFrame, Math.round(1000/config.draw_fps));
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// }
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// }
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// } else {
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// // pause the drawing as well
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// clearInterval(globals.draw_interval);
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// globals.paused = true;
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// }
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// }
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createPopulation = function(genomes) {
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var walkers = [];
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var agents = []
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for(var k = 0; k < config.population_size; k++) {
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var agent = new Agent({}, globals)
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agents.push(agent);
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walkers.push(agent.walker)
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}
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return [agents, walkers];
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}
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populationSimulationStep = function() {
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for(var k = 0; k < config.population_size; k++) {
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globals.agents[k].step()
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}
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var steps = globals.agents[0].walker.steps
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if ((steps!==0) && (0 == steps % config.round_length)) {
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resetSimulation()
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}
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}
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function saveAs(dv, name) {
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var a;
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if (typeof window.downloadAnchor == 'undefined') {
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a = window.downloadAnchor = document.createElement("a");
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a.style = "display: none";
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document.body.appendChild(a);
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} else {
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a = window.downloadAnchor
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}
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var blob = new Blob([dv], { type: 'application/octet-binary' }),
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tmpURL = window.URL.createObjectURL(blob);
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a.href = tmpURL;
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a.download = name;
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a.click();
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window.URL.revokeObjectURL(tmpURL);
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a.href = "";
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}
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downloadBrain = function (n) {
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var ts = (new Date()).toISOString().replace(':','_')
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var buf = globals.agents[n].brain.export()
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saveAs(new DataView(buf), 'walker_brain_'+n+'_'+ts+'.bin')
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};
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readBrain = function (buf) {
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var input = event.target;
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var reader = new FileReader();
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reader.onload = function(){
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var buffer = reader.result
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var imported = window.neurojs.NetOnDisk.readMultiPart(buffer)
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for (var i = 0; i < globals.agents.length; i++) {
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globals.agents[i].brain.algorithm.actor.set(imported.actor.clone())
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globals.agents[i].brain.algorithm.critic.set(imported.critic)
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}
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};
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reader.readAsArrayBuffer(input.files[0]);
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};
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updateIfLearning = function (value) {
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for (var i = 0; i < globals.agents.length; i++) {
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globals.agents[i].brain.learning = value
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}
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};
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