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<h1><a href="https://nn.labml.ai/rl/ppo/index.html">近端策略优化-PPO</a></h1>
<p>这是<a href="https://papers.labml.ai/paper/1707.06347">近端策略优化-PPO 的</a> P <a href="https://pytorch.org">yTorch</a> 实现。</p>
<p>PPO 是一种用于强化学习的策略梯度方法。简单的策略梯度方法可以对每个样本（或一组样本）进行一次梯度更新。对单个样本执行多个梯度步骤会导致问题，因为策略偏差太大，从而产生了糟糕的策略。PPO 允许我们尝试使策略接近用于采样数据的策略，从而为每个样本执行多次梯度更新。如果更新的策略与用于采样数据的策略不接近，则通过剪切梯度流来实现此目的。</p>
<p>你可以<a href="https://nn.labml.ai/rl/ppo/experiment.html">在这里</a>找到一个使用它的实验。该实验使用<a href="https://nn.labml.ai/rl/ppo/gae.html">广义优势估计</a>。</p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/ppo/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/6eff28a0910e11eb9b008db315936e2f"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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