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153 lines
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HTML
153 lines
6.0 KiB
HTML
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<meta name="description" content="This is a collection of PyTorch implementations/tutorials of reinforcement learning algorithms. It currently includes Proximal Policy Optimization, Generalized Advantage Estimation, and Deep Q Networks."/>
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<a class="parent" href="index.html">rl</a>
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/rl/__init__.py">
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<a href='#section-0'>#</a>
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<h1>Reinforcement Learning Algorithms</h1>
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<ul>
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<li><a href="ppo">Proximal Policy Optimization</a><ul>
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<li><a href="ppo/experiment.html">This is an experiment</a> that runs a PPO agent on Atari Breakout.</li>
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<li><a href="ppo/gae.html">Generalized advantage estimation</a></li>
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</ul>
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<li><a href="dqn">Deep Q Networks</a><ul>
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<li><a href="dqn/experiment.html">This is an experiment</a> that runs a DQN agent on Atari Breakout.</li>
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<li><a href="dqn/model.html">Model</a> with dueling network</li>
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</ul>
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</li>
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</ul>
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<p><a href="game.html">This is the implementation for OpenAI game wrapper</a> using <code>multiprocessing</code>.</p>
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<div class="highlight"><pre></pre></div>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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<a href="https://labml.ai">labml.ai</a>
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