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<h1 id="mixed-monte-carlo">Mixed Monte Carlo</h1>
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<p><strong>Actions space:</strong> Discrete</p>
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<p><strong>References:</strong> <a href="https://arxiv.org/abs/1703.01310">Count-Based Exploration with Neural Density Models</a></p>
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<h2 id="network-structure">Network Structure</h2>
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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>In MMC, targets are calculated as a mixture between Double DQN targets and full Monte Carlo samples (total discounted returns).</p>
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<p>The DDQN targets are calculated in the same manner as in the DDQN agent:</p>
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<script type="math/tex; mode=display"> y_t^{DDQN}=r(s_t,a_t )+\gamma Q(s_{t+1},argmax_a Q(s_{t+1},a)) </script>
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<p>The Monte Carlo targets are calculated by summing up the discounted rewards across the entire episode:</p>
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<script type="math/tex; mode=display"> y_t^{MC}=\sum_{j=0}^T\gamma^j r(s_{t+j},a_{t+j} ) </script>
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<p>A mixing ratio <script type="math/tex">\alpha</script> is then used to get the final targets:</p>
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<script type="math/tex; mode=display"> y_t=(1-\alpha)\cdot y_t^{DDQN}+\alpha \cdot y_t^{MC} </script>
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<p>Finally, the online network is trained using the current states as inputs, and the calculated targets.
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Once in every few thousand steps, copy the weights from the online network to the target network.</p>
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