Impact of Varying Item Counts on DRL Agent Performance with DASH (GPL) Student Model (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
When the number of items was increased, no major variation was observed in the performance of the DRL agent using the DASH (GPL) student model relative to the random policy, for both the likelihood and log-likelihood reward functions.
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Impact of Varying Item Counts on DRL Agent Performance with DASH (GPL) Student Model (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)