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* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
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<div class="section" id="double-dqn">
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<h1>Double DQN<a class="headerlink" href="#double-dqn" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1509.06461.pdf">Deep Reinforcement Learning with Double Q-learning</a></p>
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<div class="section" id="network-structure">
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<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline">¶</a></h2>
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<img alt="../../../_images/dqn.png" class="align-center" src="../../../_images/dqn.png" />
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</div>
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<div class="section" id="algorithm-description">
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<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="training-the-network">
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<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
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<ol class="arabic simple">
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<li>Sample a batch of transitions from the replay buffer.</li>
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<li>Using the next states from the sampled batch, run the online network in order to find the $Q$ maximizing
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action <span class="math notranslate nohighlight">\(argmax_a Q(s_{t+1},a)\)</span>. For these actions, use the corresponding next states and run the target
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network to calculate <span class="math notranslate nohighlight">\(Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span>.</li>
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<li>In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss),
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use the current states from the sampled batch, and run the online network to get the current Q values predictions.
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Set those values as the targets for the actions that were not actually played.</li>
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<li>For each action that was played, use the following equation for calculating the targets of the network:
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<span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span></li>
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<li>Finally, train the online network using the current states as inputs, and with the aforementioned targets.</li>
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<li>Once in every few thousand steps, copy the weights from the online network to the target network.</li>
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</ol>
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