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<div class="section" id="deep-q-networks">
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<h1>Deep Q Networks<a class="headerlink" href="#deep-q-networks" 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://www.cs.toronto.edu/~vmnih/docs/dqn.pdf">Playing Atari with Deep Reinforcement 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 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><p>Sample a batch of transitions from the replay buffer.</p></li>
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<li><p>Using the next states from the sampled batch, run the target network to calculate the <span class="math notranslate nohighlight">\(Q\)</span> values for each of
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the actions <span class="math notranslate nohighlight">\(Q(s_{t+1},a)\)</span>, and keep only the maximum value for each state.</p></li>
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<li><p>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.</p></li>
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<li><p>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">\(y_t=r(s_t,a_t )+\gamma \cdot max_a Q(s_{t+1})\)</span></p></li>
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<li><p>Finally, train the online network using the current states as inputs, and with the aforementioned targets.</p></li>
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<li><p>Once in every few thousand steps, copy the weights from the online network to the target network.</p></li>
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</ol>
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<dl class="class">
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<dt id="rl_coach.agents.dqn_agent.DQNAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.dqn_agent.</code><code class="descname">DQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/dqn_agent.html#DQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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