Impact of Varying Item Counts on DRL Agent Performance with EFC Student Model (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
When the number of items was increased, there was no significant increase in the performance of the DRL agent with the EFC student model relative to the random policy (using likelihood as the reward function). In the case of the log-likelihood reward function, the performance of the DRL agent with the EFC student model slightly decreased relative to the random policy.
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Impact of Varying Item Counts on DRL Agent Performance with EFC Student Model (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)