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<li class="toctree-l3"><a href="#normalized-advantage-functions">Normalized Advantage Functions</a></li>
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<h1 id="normalized-advantage-functions">Normalized Advantage Functions</h1>
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<p><strong>Actions space:</strong> Continuous</p>
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<p><strong>References:</strong> <a href="https://arxiv.org/abs/1603.00748.pdf">Continuous Deep Q-Learning with Model-based Acceleration</a></p>
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<h2 id="network-structure">Network Structure</h2>
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<h3 id="choosing-an-action">Choosing an action</h3>
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<p>The current state is used as an input to the network. The action mean <script type="math/tex"> \mu(s_t ) </script> is extracted from the output head. It is then passed to the exploration policy which adds noise in order to encourage exploration.</p>
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<h3 id="training-the-network">Training the network</h3>
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<p>The network is trained by using the following targets:
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<script type="math/tex; mode=display"> y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1}) </script>
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Use the next states as the inputs to the target network and extract the <script type="math/tex"> V </script> value, from within the head, to get <script type="math/tex"> V(s_{t+1} ) </script>. Then, update the online network using the current states and actions as inputs, and <script type="math/tex"> y_t </script> as the targets.
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After every training step, use a soft update in order to copy the weights from the online network to the target network.</p>
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