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* initial ACER commit * Code cleanup + several fixes * Q-retrace bug fix + small clean-ups * added documentation for acer * ACER benchmarks * update benchmarks table * Add nightly running of golden and trace tests. (#202) Resolves #200 * comment out nightly trace tests until values reset. * remove redundant observe ignore (#168) * ensure nightly test env containers exist. (#205) Also bump integration test timeout * wxPython removal (#207) Replacing wxPython with Python's Tkinter. Also removing the option to choose multiple files as it is unused and causes errors, and fixing the load file/directory spinner. * Create CONTRIBUTING.md (#210) * Create CONTRIBUTING.md. Resolves #188 * run nightly golden tests sequentially. (#217) Should reduce resource requirements and potential CPU contention but increases overall execution time. * tests: added new setup configuration + test args (#211) - added utils for future tests and conftest - added test args * new docs build * golden test update
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<div class="section" id="dueling-dqn">
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<h1>Dueling DQN<a class="headerlink" href="#dueling-dqn" 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/1511.06581">Dueling Network Architectures for 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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<h2>General Description<a class="headerlink" href="#general-description" title="Permalink to this headline">¶</a></h2>
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<p>Dueling DQN presents a change in the network structure comparing to DQN.</p>
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<p>Dueling DQN uses a specialized <em>Dueling Q Head</em> in order to separate <span class="math notranslate nohighlight">\(Q\)</span> to an <span class="math notranslate nohighlight">\(A\)</span> (advantage)
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stream and a <span class="math notranslate nohighlight">\(V\)</span> stream. Adding this type of structure to the network head allows the network to better differentiate
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actions from one another, and significantly improves the learning.</p>
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<p>In many states, the values of the different actions are very similar, and it is less important which action to take.
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This is especially important in environments where there are many actions to choose from. In DQN, on each training
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iteration, for each of the states in the batch, we update the <a href="#id1"><span class="problematic" id="id2">:ath:`Q`</span></a> values only for the specific actions taken in
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those states. This results in slower learning as we do not learn the <span class="math notranslate nohighlight">\(Q\)</span> values for actions that were not taken yet.
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On dueling architecture, on the other hand, learning is faster - as we start learning the state-value even if only a
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single action has been taken at this state.</p>
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