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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="hierarchical-actor-critic">
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<h1>Hierarchical Actor Critic<a class="headerlink" href="#hierarchical-actor-critic" 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/1805.08180">Hierarchical Reinforcement Learning with Hindsight</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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<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 an action mean vector <span class="math notranslate nohighlight">\(\mu\)</span>.
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While in training phase, use a continuous exploration policy, such as the Ornstein-Uhlenbeck process,
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to add exploration noise to the action. When testing, use the mean vector <span class="math notranslate nohighlight">\(\mu\)</span> as-is.</p>
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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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