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<h1 id="categorical-dqn">Categorical DQN</h1>
<p><strong>Actions space:</strong> Discrete</p>
<p><strong>References:</strong> <a href="https://arxiv.org/abs/1707.06887">A Distributional Perspective on Reinforcement Learning</a></p>
<h2 id="network-structure">Network Structure</h2>
<p style="text-align: center;">
<img src="..\..\design_imgs\distributional_dqn.png">
</p>
<h2 id="algorithm-description">Algorithm Description</h2>
<h3 id="training-the-network">Training the network</h3>
<ol>
<li>Sample a batch of transitions from the replay buffer. </li>
<li>
<p>The Bellman update is projected to the set of atoms representing the <script type="math/tex"> Q </script> values distribution, such that the <script type="math/tex">i-th</script> component of the projected update is calculated as follows:
<script type="math/tex; mode=display"> (\Phi \hat{T} Z_{\theta}(s_t,a_t))_i=\sum_{j=0}^{N-1}\Big[1-\frac{|[\hat{T}_{z_{j}}]^{V_{MAX}}_{V_{MIN}}-z_i|}{\Delta z}\Big]^1_0 \ p_j(s_{t+1}, \pi(s_{t+1})) </script>
where:</p>
<ul>
<li>
<script type="math/tex">[ \cdot ] </script> bounds its argument in the range [a, b]</li>
<li>
<script type="math/tex">\hat{T}_{z_{j}}</script> is the Bellman update for atom <script type="math/tex">z_j</script>: &nbsp; &nbsp; <script type="math/tex">\hat{T}_{z_{j}} := r+\gamma z_j</script>
</li>
</ul>
</li>
<li>
<p>Network is trained with the cross entropy loss between the resulting probability distribution and the target probability distribution. Only the target of the actions that were actually taken is updated. </p>
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<li>Once in every few thousand steps, weights are copied from the online network to the target network.</li>
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