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<div class="section" id="dueling-dqn">
<h1>Dueling DQN<a class="headerlink" href="#dueling-dqn" title="Permalink to this headline"></a></h1>
<p><strong>Actions space:</strong> Discrete</p>
<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1511.06581">Dueling Network Architectures for Deep Reinforcement Learning</a></p>
<div class="section" id="network-structure">
<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline"></a></h2>
<img alt="../../../_images/dueling_dqn.png" class="align-center" src="../../../_images/dueling_dqn.png" />
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<div class="section" id="general-description">
<h2>General Description<a class="headerlink" href="#general-description" title="Permalink to this headline"></a></h2>
<p>Dueling DQN presents a change in the network structure comparing to DQN.</p>
<p>Dueling DQN uses a specialized <em>Dueling Q Head</em> in order to separate <span class="math notranslate nohighlight">\(Q\)</span> to an <span class="math notranslate nohighlight">\(A\)</span> (advantage)
stream and a <span class="math notranslate nohighlight">\(V\)</span> stream. Adding this type of structure to the network head allows the network to better differentiate
actions from one another, and significantly improves the learning.</p>
<p>In many states, the values of the different actions are very similar, and it is less important which action to take.
This is especially important in environments where there are many actions to choose from. In DQN, on each training
iteration, for each of the states in the batch, we update the <a href="#id1"><span class="problematic" id="id2">:ath:`Q`</span></a> values only for the specific actions taken in
those states. This results in slower learning as we do not learn the <span class="math notranslate nohighlight">\(Q\)</span> values for actions that were not taken yet.
On dueling architecture, on the other hand, learning is faster - as we start learning the state-value even if only a
single action has been taken at this state.</p>
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