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docs/index.html

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<li><a href="gan/stylegan/index.html">StyleGAN 2</a></li>
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</ul>
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<h4><a href="sketch_rnn/index.html">Sketch RNN</a></h4>
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<h4><a href="cfr/index.html">Counterfactual Regret Minimization (CFR)</a></h4>
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<p>Solving games with incomplete information such as poker with CFR.</p>
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<ul>
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<li><a href="cfr/kuhn/index.html">Kuhn Poker</a></li>
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</ul>
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<h4><a href="rl/index.html">Reinforcement Learning</a></h4>
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<ul>
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<li><a href="rl/ppo/index.html">Proximal Policy Optimization</a> with

labml_nn/__init__.py

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#### ✨ [Sketch RNN](sketch_rnn/index.html)
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#### ✨ [Counterfactual Regret Minimization (CFR)](cfr/index.html)
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Solving games with incomplete information such as poker with CFR.
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* [Kuhn Poker](cfr/kuhn/index.html)
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#### ✨ [Reinforcement Learning](rl/index.html)
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* [Proximal Policy Optimization](rl/ppo/index.html) with
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[Generalized Advantage Estimation](rl/ppo/gae.html)

readme.md

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#### [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
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#### [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
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Solving games with incomplete information such as poker with CFR.
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* [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
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#### [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
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* [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
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[Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)

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