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<a href="../../../_sources/components/agents/imitation/cil.rst.txt" rel="nofollow"> View page source</a>
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<div class="section" id="conditional-imitation-learning">
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<h1>Conditional Imitation Learning<a class="headerlink" href="#conditional-imitation-learning" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete | Continuous</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1710.02410">End-to-end Driving via Conditional Imitation 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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<img alt="../../../_images/cil.png" class="align-center" src="../../../_images/cil.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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<p>The replay buffer contains the expert demonstrations for the task.
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These demonstrations are given as state, action tuples, and with no reward.
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The training goal is to reduce the difference between the actions predicted by the network and the actions taken by
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the expert for each state.
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In conditional imitation learning, each transition is assigned a class, which determines the goal that was pursuit
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in that transitions. For example, 3 possible classes can be: turn right, turn left and follow lane.</p>
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<ol class="arabic simple">
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<li><p>Sample a batch of transitions from the replay buffer, where the batch is balanced, meaning that an equal number
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of transitions will be sampled from each class index.</p></li>
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<li><p>Use the current states as input to the network, and assign the expert actions as the targets of the network heads
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corresponding to the state classes. For the other heads, set the targets to match the currently predicted values,
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so that the loss for the other heads will be zeroed out.</p></li>
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<li><p>We use a regression head, that minimizes the MSE loss between the network predicted values and the target values.</p></li>
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</ol>
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<dl class="class">
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<dt id="rl_coach.agents.cil_agent.CILAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.cil_agent.</code><code class="descname">CILAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/cil_agent.html#CILAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.cil_agent.CILAlgorithmParameters" 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"><p><strong>state_key_with_the_class_index</strong> – (str)
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The key of the state dictionary which corresponds to the value that will be used to control the class index.</p>
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</dd>
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</dl>
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</dd></dl>
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