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docs/rl/dqn/index.html

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handleImages()
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</script>
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</body>
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</html>
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</html>

docs/sitemap.xml

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<url>
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<loc>https://nn.labml.ai/diffusion/stable_diffusion/latent_diffusion.html</loc>
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<lastmod>2022-09-15T16:30:00+00:00</lastmod>
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<lastmod>2022-12-21T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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<url>
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<loc>https://nn.labml.ai/transformers/mha.html</loc>
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<lastmod>2022-09-07T16:30:00+00:00</lastmod>
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<lastmod>2022-12-24T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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labml_nn/rl/dqn/__init__.py

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### Target network 🎯
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In order to improve stability we use experience replay that randomly sample
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from previous experience $U(D)$. We also use a Q network
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with a separate set of paramters $\textcolor{orange}{\theta_i^{-}}$ to calculate the target.
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with a separate set of parameters $\textcolor{orange}{\theta_i^{-}}$ to calculate the target.
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$\textcolor{orange}{\theta_i^{-}}$ is updated periodically.
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This is according to paper
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[Human Level Control Through Deep Reinforcement Learning](https://deepmind.com/research/dqn/).

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