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Varuna Jayasiri
2021-08-08 08:32:39 +05:30
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@ -78,7 +78,7 @@ introduces Monte Carlo Counterfactual Regret Minimization (<strong>MCCFR</strong
where we sample from the game tree and estimate the regrets.</p>
<p>We tried to keep our Python implementation easy-to-understand like a tutorial.
We run it on <a href="kuhn/index.html">a very simple imperfect information game called Kuhn poker</a>.</p>
<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
<p><a href="https://twitter.com/labmlai/status/1407186002255380484"><img alt="Twitter thread" src="https://img.shields.io/twitter/url?style=social&amp;url=https%3A%2F%2Ftwitter.com%2Flabmlai%2Fstatus%2F1407186002255380484" /></a>
Twitter thread</p>
<h2>Introduction</h2>

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@ -88,7 +88,7 @@ This game is played repeatedly and a good strategy will optimize for the long te
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<p>He we extend the <code>InfoSet</code> class and <code>History</code> class defined in <a href="../index.html"><code>__init__.py</code></a>
with Kuhn Poker specifics.</p>
<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
<a href="https://app.labml.ai/run/7c35d3fad29711eba588acde48001122"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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