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<h1><a href="https://nn.labml.ai/rl/ppo/index.html">近端策略优化-PPO</a></h1>
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<p>这是 P <a href="https://pytorch.org">yTorch</a> 实现的<a href="https://arxiv.org/abs/1707.06347">近端策略优化-PPO</a>。</p>
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<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，因为该策略偏离得太大，从而产生了错误的策略。PPO 允许我们在每个样本中进行多次梯度更新，方法是尽量使策略与用于采样数据的策略保持一致。如果更新后的策略与用于采样数据的策略不接近，则通过削减梯度流来实现此目的。</p>
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<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>
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