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* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
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<div class="section" id="wolpertinger">
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<h1>Wolpertinger<a class="headerlink" href="#wolpertinger" 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/1512.07679">Deep Reinforcement Learning in Large Discrete Action Spaces</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/wolpertinger.png" class="align-center" src="../../../_images/wolpertinger.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="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>Pass the current states through the actor network, and get a proto action <span class="math notranslate nohighlight">\(\mu\)</span>.
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While in training phase, use a continuous exploration policy, such as the a gaussian noise,
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to add exploration noise to the proto action. Then, pass the proto action to a k-NN tree to find actual valid
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action candidates, which are in the surrounding neighborhood of the proto action. Those actions are then passed to the
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critic to evaluate their goodness, and eventually the discrete index of the action with the highest Q value is chosen.
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When testing, the same flow is used, but no exploration noise is added.</p>
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</div>
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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>Training the network is exactly the same as in DDPG. Unlike when choosing the action, the proto action is not passed
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through the k-NN tree. It is being passed directly to the critic.</p>
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<p>Start by sampling a batch of transitions from the experience replay.</p>
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<ul>
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<li><p>To train the <strong>critic network</strong>, use the following targets:</p>
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<p><span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))\)</span></p>
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<p>First run the actor target network, using the next states as the inputs, and get <span class="math notranslate nohighlight">\(\mu (s_{t+1} )\)</span>.
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Next, run the critic target network using the next states and <span class="math notranslate nohighlight">\(\mu (s_{t+1} )\)</span>, and use the output to
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calculate <span class="math notranslate nohighlight">\(y_t\)</span> according to the equation above. To train the network, use the current states and actions
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as the inputs, and <span class="math notranslate nohighlight">\(y_t\)</span> as the targets.</p>
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</li>
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<li><p>To train the <strong>actor network</strong>, use the following equation:</p>
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<p><span class="math notranslate nohighlight">\(\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]\)</span></p>
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<p>Use the actor’s online network to get the action mean values using the current states as the inputs.
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Then, use the critic online network in order to get the gradients of the critic output with respect to the
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action mean values <span class="math notranslate nohighlight">\(\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }\)</span>.
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Using the chain rule, calculate the gradients of the actor’s output, with respect to the actor weights,
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given <span class="math notranslate nohighlight">\(\nabla_a Q(s,a)\)</span>. Finally, apply those gradients to the actor network.</p>
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</li>
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</ul>
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<p>After every training step, do a soft update of the critic and actor target networks’ weights from the online networks.</p>
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<dl class="class">
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<dt id="rl_coach.agents.wolpertinger_agent.WolpertingerAlgorithmParameters">
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<em class="property">class </em><code class="sig-prename descclassname">rl_coach.agents.wolpertinger_agent.</code><code class="sig-name descname">WolpertingerAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/wolpertinger_agent.html#WolpertingerAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.wolpertinger_agent.WolpertingerAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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