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| <!doctype html> | ||
| <html> | ||
| <head> | ||
| <meta charset="utf-8"> | ||
| <title>Metacar: Continous control in traffic</title> | ||
| <link rel="icon" href="/public/img/icon.png"> | ||
| <link rel="stylesheet" href="/public/css/general.css"> | ||
| <link href="https://fonts.googleapis.com/css?family=Tajawal" rel="stylesheet"> | ||
| </head> | ||
| <body> | ||
|
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||
| <header> | ||
| <div class="header_container"> | ||
| <h1><a href="/">Metacar</a></h1> | ||
| <a href="https://github.com/thibo73800/metacar"><img src="/public/img/github-logo.png" /></a> | ||
| </div> | ||
| </header> | ||
|
|
||
| <div class="canvas_container"> | ||
| <!-- | ||
| You just have to add a div with an ID as bellow and include the js to start | ||
| using metacar. --> | ||
| <div class="canvas" id="canvas"></div> | ||
| </div> | ||
|
|
||
| <div class="body_container"> | ||
| <h3 id="trainingProgress"></h3> | ||
| <h3 style="float: left" >Current state (Lidar points)</h3><br><br><br> | ||
| <div id="realtime_viewer" style="float: left"></div> | ||
| <p> | ||
| The algorithm is based on the following papers: | ||
| <ul style="margin:0;"> | ||
| <li><b>Deep Deterministic Policy Gradients (DDPG): <a href="https://arxiv.org/abs/1509.02971">paper</a></b> </li> | ||
| <li><b>Parameter Space Noise for Exploration</b>: <a href="https://blog.openai.com/better-exploration-with-parameter-noise/">paper</a> </li> | ||
| </ul> | ||
| <br> | ||
| You can use the <b>arrow keys</b> to control the car by yourself.<br><br> | ||
|
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| The motion control is based on two continuous values for the throttle and steering angle of the car. | ||
|
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| <br><br>The left window gives you an overview of what the autonomous vehicle (in red) sees.<br><br> | ||
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| You can find the code of this demo <a href="https://github.com/thibo73800/metacar/tree/master/demo/webapp/public/js/ddpg/">here</a> and <a href="https://github.com/thibo73800/metacar/tree/master/demo/webapp/public/js/ddpg-traffic/">here</a>. | ||
| </p> | ||
| </div> | ||
|
|
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| <br> | ||
| <div class="body_container" id="statContainer" style="position:relative"></div> | ||
|
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||
| <script src="https://canvasjs.com/assets/script/canvasjs.min.js"></script> | ||
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"> </script> | ||
| <script src="https://cdnjs.cloudflare.com/ajax/libs/pixi.js/4.7.1/pixi.min.js"></script> | ||
| <script src="/dist/metacar.min.js"></script> | ||
|
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||
| <script type="text/javascript" src="/public/js/utils.js"></script> | ||
| <script type="text/javascript" src="/public/js/viewer.js"></script> | ||
|
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| <script type="text/javascript" src="/public/js/ddpg/models.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/memory.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/prioritized_memory.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/noise.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/ddpg.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/ddpg_agent.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg-traffic/index.js"></script> | ||
| </body> | ||
| </html> |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,66 @@ | ||
| <!doctype html> | ||
| <html> | ||
| <head> | ||
| <meta charset="utf-8"> | ||
| <title>Metacar: Continous control</title> | ||
| <link rel="icon" href="/public/img/icon.png"> | ||
| <link rel="stylesheet" href="/public/css/general.css"> | ||
| <link href="https://fonts.googleapis.com/css?family=Tajawal" rel="stylesheet"> | ||
| </head> | ||
| <body> | ||
|
|
||
| <header> | ||
| <div class="header_container"> | ||
| <h1><a href="/">Metacar</a></h1> | ||
| <a href="https://github.com/thibo73800/metacar"><img src="/public/img/github-logo.png" /></a> | ||
| </div> | ||
| </header> | ||
|
|
||
| <div class="canvas_container"> | ||
| <!-- | ||
| You just have to add a div with an ID as bellow and include the js to start | ||
| using metacar. --> | ||
| <div class="canvas" id="canvas"></div> | ||
| </div> | ||
|
|
||
| <div class="body_container"> | ||
| <h3 id="trainingProgress"></h3> | ||
| <h3 style="float: left" >Current state (Lidar points)</h3><br><br><br> | ||
| <div id="realtime_viewer" style="float: left"></div> | ||
| <p> | ||
| The algorithm is based on the following papers: | ||
| <ul style="margin:0;"> | ||
| <li><b>Deep Deterministic Policy Gradients (DDPG): <a href="https://arxiv.org/abs/1509.02971">paper</a></b> </li> | ||
| <li><b>Parameter Space Noise for Exploration</b>: <a href="https://blog.openai.com/better-exploration-with-parameter-noise/">paper</a> </li> | ||
| </ul> | ||
| <br> | ||
| You can use the <b>arrow keys</b> to control the car by yourself.<br><br> | ||
|
|
||
| The motion control is based on two continuous values for the throttle and steering angle of the car. | ||
|
|
||
| <br><br>The left window gives you an overview of what the autonomous vehicle (in red) sees.<br><br> | ||
|
|
||
| You can find the code of this demo <a href="https://github.com/thibo73800/metacar/tree/master/demo/webapp/public/js/ddpg/">here</a>. | ||
| </p> | ||
| </div> | ||
|
|
||
| <br> | ||
| <div class="body_container" id="statContainer" style="position:relative"></div> | ||
|
|
||
| <script src="https://canvasjs.com/assets/script/canvasjs.min.js"></script> | ||
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]"> </script> | ||
| <script src="https://cdnjs.cloudflare.com/ajax/libs/pixi.js/4.7.1/pixi.min.js"></script> | ||
| <script src="/dist/metacar.min.js"></script> | ||
|
|
||
| <script type="text/javascript" src="/public/js/utils.js"></script> | ||
| <script type="text/javascript" src="/public/js/viewer.js"></script> | ||
|
|
||
| <script type="text/javascript" src="/public/js/ddpg/models.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/prioritized_memory.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/memory.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/noise.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/ddpg.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/ddpg_agent.js"></script> | ||
| <script type="text/javascript" src="/public/js/ddpg/index.js"></script> | ||
| </body> | ||
| </html> |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,73 @@ | ||
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| let levelUrl = metacar.level.level3; | ||
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| var env = new metacar.env("canvas", levelUrl); | ||
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| env.setAgentMotion(metacar.motion.ControlMotion, {}); | ||
| env.setAgentLidar({pts: 7, width: 3, height: 7, pos: -0.5}) | ||
|
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| // js/DDPG/ddpg.js | ||
| var agent = new DDPGAgent(env, { | ||
| stateSize: 50, | ||
| resetEpisode: true, | ||
| desiredActionStddev: 0.4, | ||
| initialStddev: 0.4, | ||
| actorFirstLayerSize: 128, | ||
| actorSecondLayerSize: 64, | ||
| criticFirstLayerSSize: 128, | ||
| criticFirstLayerASize: 128, | ||
| criticSecondLayerSize: 64, | ||
| nbEpochs: 1000 | ||
| }); | ||
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| initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); | ||
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| let it = 0; | ||
| env.loop(() => { | ||
| let state = env.getState(); | ||
| displayState("realtime_viewer", state.lidar, 200, 200); | ||
| let reward = env.getLastReward(); | ||
| const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); | ||
| if (it % 10 == 0) | ||
| displayScores("realtime_viewer", [qValue, state.a, state.steering], reward, ["Q(a, s)", "Acceleration", "Steering Angle"]); | ||
| it += 1; | ||
| }); | ||
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| env.load().then(() => { | ||
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| env.addEvent("train [Background]", () => { | ||
| let train = confirm("The training process takes some time and might slow this tab. Do you want to continue? \n You can also load a pre-trained model."); | ||
| if (train){ | ||
| env.render(false); | ||
| agent.train(false); | ||
| } | ||
| }); | ||
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| env.addEvent("Train [Show the training]", () => { | ||
| env.steping(false); | ||
| agent.train(true); | ||
| }); | ||
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| env.addEvent("play", () => { | ||
| agent.play(); | ||
| }); | ||
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| env.addEvent("stop", () => { | ||
| agent.stop(); | ||
| }); | ||
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| env.addEvent("reset_env"); | ||
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| env.addEvent("save", () => { | ||
| agent.save("model-ddpg-traffic"); | ||
| }); | ||
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| env.addEvent("load", () => { | ||
| agent.restore("ddpg-traffic", "model-ddpg-traffic-epoch-120"); | ||
| }); | ||
| }); | ||
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