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<h1><a href="https://nn.labml.ai/rl/ppo/index.html">Proximal Policy Optimization - PPO</a></h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of
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<a href="https://arxiv.org/abs/1707.06347">Proximal Policy Optimization - PPO</a>.</p>
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<p>PPO is a policy gradient method for reinforcement learning.
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Simple policy gradient methods one do a single gradient update per sample (or a set of samples).
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Doing multiple gradient steps for a singe sample causes problems
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because the policy deviates too much producing a bad policy.
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PPO lets us do multiple gradient updates per sample by trying to keep the
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policy close to the policy that was used to sample data.
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It does so by clipping gradient flow if the updated policy
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is not close to the policy used to sample the data.</p>
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<p>You can find an experiment that uses it <a href="https://nn.labml.ai/rl/ppo/experiment.html">here</a>.
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The experiment uses <a href="https://nn.labml.ai/rl/ppo/gae.html">Generalized Advantage Estimation</a>.</p>
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<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/rl/ppo/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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