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* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
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<div class="section" id="quantile-regression-dqn">
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<h1>Quantile Regression DQN<a class="headerlink" href="#quantile-regression-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/1710.10044">Distributional Reinforcement Learning with Quantile Regression</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/qr_dqn.png" class="align-center" src="../../../_images/qr_dqn.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="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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<ol class="arabic simple">
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<li>Sample a batch of transitions from the replay buffer.</li>
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<li>First, the next state quantiles are predicted. These are used in order to calculate the targets for the network,
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by following the Bellman equation.
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Next, the current quantile locations for the current states are predicted, sorted, and used for calculating the
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quantile midpoints targets.</li>
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<li>The network is trained with the quantile regression loss between the resulting quantile locations and the target
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quantile locations. Only the targets of the actions that were actually taken are updated.</li>
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<li>Once in every few thousand steps, weights are copied from the online network to the target network.</li>
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</ol>
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<dl class="class">
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<dt id="rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.qr_dqn_agent.</code><code class="descname">QuantileRegressionDQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/qr_dqn_agent.html#QuantileRegressionDQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd><table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
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<li><strong>atoms</strong> – (int)
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the number of atoms to predict for each action</li>
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<li><strong>huber_loss_interval</strong> – (float)
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One of the huber loss parameters, and is referred to as <span class="math notranslate nohighlight">\(\kapa\)</span> in the paper.
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It describes the interval [-k, k] in which the huber loss acts as a MSE loss.</li>
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</ul>
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</td>
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</tr>
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