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<div class="section" id="soft-actor-critic">
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<h1>Soft Actor-Critic<a class="headerlink" href="#soft-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/1801.01290">Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor</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/sac.png" class="align-center" src="../../../_images/sac.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-continuous-actions">
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<h3>Choosing an action - Continuous actions<a class="headerlink" href="#choosing-an-action-continuous-actions" title="Permalink to this headline">¶</a></h3>
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<p>The policy network is used in order to predict mean and log std for each action. While training, a sample is taken
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from a Gaussian distribution with these mean and std values. When testing, the agent can choose deterministically
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by picking the mean value or sample from a gaussian distribution like in training.</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>Start by sampling a batch <span class="math notranslate nohighlight">\(B\)</span> of transitions from the experience replay.</p>
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<ul>
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<li><p>To train the <strong>Q network</strong>, use the following targets:</p>
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<div class="math notranslate nohighlight">
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\[y_t^Q=r(s_t,a_t)+\gamma \cdot V(s_{t+1})\]</div>
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<p>The state value used in the above target is acquired by running the target state value network.</p>
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</li>
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<li><p>To train the <strong>State Value network</strong>, use the following targets:</p>
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<div class="math notranslate nohighlight">
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\[y_t^V = \min_{i=1,2}Q_i(s_t,\tilde{a}) - log\pi (\tilde{a} \vert s),\,\,\,\, \tilde{a} \sim \pi(\cdot \vert s_t)\]</div>
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<p>The state value network is trained using a sample-based approximation of the connection between and state value and state
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action values, The actions used for constructing the target are <strong>not</strong> sampled from the replay buffer, but rather sampled
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from the current policy.</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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<div class="math notranslate nohighlight">
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\[\nabla_{\theta} J \approx \nabla_{\theta} \frac{1}{\vert B \vert} \sum_{s_t\in B} \left( Q \left(s_t, \tilde{a}_\theta(s_t)\right) - log\pi_{\theta}(\tilde{a}_{\theta}(s_t)\vert s_t) \right),\,\,\,\, \tilde{a} \sim \pi(\cdot \vert s_t)\]</div>
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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 V target network’s weights from the online networks.</p>
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<dl class="class">
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<dt id="rl_coach.agents.soft_actor_critic_agent.SoftActorCriticAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.soft_actor_critic_agent.</code><code class="descname">SoftActorCriticAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/soft_actor_critic_agent.html#SoftActorCriticAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.soft_actor_critic_agent.SoftActorCriticAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd><dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>num_steps_between_copying_online_weights_to_target</strong> – (StepMethod)
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The number of steps between copying the online network weights to the target network weights.</p></li>
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<li><p><strong>rate_for_copying_weights_to_target</strong> – (float)
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When copying the online network weights to the target network weights, a soft update will be used, which
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weight the new online network weights by rate_for_copying_weights_to_target. (Tau as defined in the paper)</p></li>
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<li><p><strong>use_deterministic_for_evaluation</strong> – (bool)
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If True, during the evaluation phase, action are chosen deterministically according to the policy mean
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and not sampled from the policy distribution.</p></li>
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</ul>
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</dd>
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</dl>
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