rl_2d_walker.js/src/index.html

<!doctype html>
<html>
  <head>
    <title>Reinforcement Learning 2D Biped Walkers</title>
    <link rel="stylesheet" href="css/walkers.css" type="text/css" />
     
  </head>
  <body>
    <div id="main_holder">
      <h1>Reinforcement Learning 2D Humanoid Walkers</h1>
      <h3 id="page_quote">"Play that funky music, robot"</h3>
      <canvas id="main_screen" width="800" height="350"></canvas>
      <div id="generation_timer"><div id="generation_timer_bar">&nbsp;</div></div>

      <div id="info"></div>

		<div class="row">
			<button onclick="window.downloadBrain(0);">Save agent</button>
			<button onclick="document.getElementById('brainFile').click();">Open brain file</button>
		</div>

    <input id="brainFile" type='file' accept='*/*' onchange='window.readBrain(event)' style="display: none">
    
    <div style="margin-top: 20px"><input type="checkbox" onclick="window.updateIfLearning(this.checked);" checked>Learning</div>
    <div id="info">
      This is an experiment to teach 2d humanoids to walk and to display their progress in the browser. 
    <p></p><br/>
      This uses use reinforcement learning, a branch of machine learning target at controlling systems, to teach them to walk. Training is done offline in tensorflow.js.
      <p></p><br/>
      Credits: The walker code is adapted from <a href="http://rednuht.org/genetic_walkers/">rednuht.org/genetic_walkers/</a>, the DDPG code was adapted from <a href="https://github.com/thibo73800/metacar">github.com/thibo73800/metacar</a>.
    </div>

    <!-- <div id="stats-wrapper">
    <h3>Progress</h3>
    <pre id="stats-prog"></pre>
    <h3>Agent 1</h3>
    <pre id="stats-ag0"></pre>
    </div> -->

    <!-- <div id="charts"></div>

    </div> -->
  </body>
  <script src="../dist/vendor.bundle.js"></script>
  <script src="../dist/lib.bundle.js"></script>
  <script src="../dist/app.bundle.js"></script>
</html>