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  .env
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  frozen_lake_env
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- __pycache__
 
 
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  .env
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  frozen_lake_env
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+ __pycache__
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+ node_modules
assets/eval_screenshot_train_20k.png ADDED
assets/eval_screenshot_train_2k.png ADDED
index.html ADDED
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+ <!DOCTYPE html>
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+ <html>
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+
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+ <head>
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+ <meta charset="utf-8">
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+ <title>Frozen Lake</title>
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+ <meta name="viewport" content="width=device-width, initial-scale=1">
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+ <style>
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+ body {
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+ background-color: #000;
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+ }
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+
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+ #container {
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+ margin: auto;
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+ max-width: 800px;
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+ text-align: center;
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+ }
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+
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+ #container>img {
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+ width: 100%
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+ }
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+
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+ #container>a,
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+ #container>h2,
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+ #container>p {
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+ color: #fff;
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+ }
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+
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+ #container>a {
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+ margin-top: 16px;
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+ }
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+ </style>
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+ </head>
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+
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+ <body>
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+ <div id="container">
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+ <h2>RL - Slippery Frozen Lake Q-Learning</h2>
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+ <p>I trained a Q-Learning model on the OpenAI Gym Slippery Frozen Lake environment for 20,000 iterations, and
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+ evaluated for 1,000 iterations.
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+ The trained model had a success rate of about 73%. Action for the 3 column on the second row is especially
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+ interesting,
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+ it correctly learns that the best policy is to try move toward one of the holes because there is a 1/3
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+ chance of slipping and slips are orthogonal to the desired direction.</p>
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+ <img src="assets/eval_screenshot_train_20k.png" alt="Q-Learning Agent">
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+ <a href="https://www.youtube.com/watch?v=b1oh3TK6Jhg">Training and Evaluation Video (youtube)</a>
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+ </div>
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+ </body>
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+
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+ </html>