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Denoising Diffusion Probabilistic Models (#98)
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Varuna Jayasiri
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docs/rl/dqn/readme.html
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docs/rl/dqn/readme.html
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<title>Deep Q Networks (DQN)</title>
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<a href='#section-0'>#</a>
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<h1><a href="https://nn.labml.ai/rl/dqn/index.html">Deep Q Networks (DQN)</a></h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of paper
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<a href="https://papers.labml.ai/paper/1312.5602">Playing Atari with Deep Reinforcement Learning</a>
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along with <a href="https://nn.labml.ai/rl/dqn/model.html">Dueling Network</a>, <a href="https://nn.labml.ai/rl/dqn/replay_buffer.html">Prioritized Replay</a>
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and Double Q Network.</p>
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<p>Here is the <a href="https://nn.labml.ai/rl/dqn/experiment.html">experiment</a> and <a href="https://nn.labml.ai/rl/dqn/model.html">model</a> implementation.</p>
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<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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