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<li class="toctree-l3"><a href="#clipped-proximal-policy-optimization">Clipped Proximal Policy Optimization</a></li>
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<h1 id="clipped-proximal-policy-optimization">Clipped Proximal Policy Optimization</h1>
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<p><strong>Actions space:</strong> Discrete|Continuous</p>
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<p><strong>References:</strong> <a href="https://arxiv.org/pdf/1707.06347.pdf">Proximal Policy Optimization Algorithms</a></p>
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<h2 id="network-structure">Network Structure</h2>
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<p style="text-align: center;">
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<img src="..\..\design_imgs\ppo.png">
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</p>
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<h2 id="algorithm-description">Algorithm Description</h2>
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<h3 id="choosing-an-action-continuous-action">Choosing an action - Continuous action</h3>
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<p>Same as in PPO. </p>
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<h3 id="training-the-network">Training the network</h3>
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<p>Very similar to PPO, with several small (but very simplifying) changes:</p>
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<ol>
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<li>
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<p>Train both the value and policy networks, simultaneously, by defining a single loss function, which is the sum of each of the networks loss functions. Then, back propagate gradients only once from this unified loss function.</p>
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<p>The unified network's optimizer is set to Adam (instead of L-BFGS for the value network as in PPO). </p>
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<p>Value targets are now also calculated based on the GAE advantages. In this method, the <script type="math/tex"> V </script> values are predicted from the critic network, and then added to the GAE based advantages, in order to get a <script type="math/tex"> Q </script> value for each action. Now, since our critic network is predicting a <script type="math/tex"> V </script> value for each state, setting the <script type="math/tex"> Q </script> calculated action-values as a target, will on average serve as a <script type="math/tex"> V </script> state-value target. </p>
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<p>Instead of adapting the penalizing KL divergence coefficient used in PPO, the likelihood ratio <script type="math/tex">r_t(\theta) =\frac{\pi_{\theta}(a|s)}{\pi_{\theta_{old}}(a|s)}</script> is clipped, to achieve a similar effect. This is done by defining the policy's loss function to be the minimum between the standard surrogate loss and an epsilon clipped surrogate loss:</p>
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<p>
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<script type="math/tex; mode=display">L^{CLIP}(\theta)=E_{t}[min(r_t(\theta)\cdot \hat{A}_t, clip(r_t(\theta), 1-\epsilon, 1+\epsilon) \cdot \hat{A}_t)] </script>
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