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<h1 id="behavioral-cloning">Behavioral Cloning</h1>
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<p><strong>Actions space:</strong> Discrete|Continuous</p>
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<h2 id="network-structure">Network Structure</h2>
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<img src="..\..\design_imgs\dqn.png">
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<h2 id="algorithm-description">Algorithm Description</h2>
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<h3 id="training-the-network">Training the network</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 the expert for each state.</p>
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<ol>
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<li>Sample a batch of transitions from the replay buffer.</li>
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<li>Use the current states as input to the network, and the expert actions as the targets of the network.</li>
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<li>The loss function for the network is MSE, and therefore we use the Q head to minimize this loss.</li>
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