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<div class="section" id="normalized-advantage-functions">
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<h1>Normalized Advantage Functions<a class="headerlink" href="#normalized-advantage-functions" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Continuous</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1603.00748.pdf">Continuous Deep Q-Learning with Model-based Acceleration</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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<a class="reference internal image-reference" href="../../../_images/naf.png"><img alt="../../../_images/naf.png" class="align-center" src="../../../_images/naf.png" style="width: 600px;" /></a>
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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="choosing-an-action">
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<h3>Choosing an action<a class="headerlink" href="#choosing-an-action" title="Permalink to this headline">¶</a></h3>
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<p>The current state is used as an input to the network. The action mean <span class="math notranslate nohighlight">\(\mu(s_t )\)</span> is extracted from the output head.
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It is then passed to the exploration policy which adds noise in order to encourage exploration.</p>
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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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<p>The network is trained by using the following targets:
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<span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1})\)</span>
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Use the next states as the inputs to the target network and extract the <span class="math notranslate nohighlight">\(V\)</span> value, from within the head,
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to get <span class="math notranslate nohighlight">\(V(s_{t+1} )\)</span>. Then, update the online network using the current states and actions as inputs,
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and <span class="math notranslate nohighlight">\(y_t\)</span> 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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<dl class="class">
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<dt id="rl_coach.agents.naf_agent.NAFAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.naf_agent.</code><code class="descname">NAFAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/naf_agent.html#NAFAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.naf_agent.NAFAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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