Comparison between likelihood and average of sum of outcomes based reward functions (research objective) (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
"DRL agents were first trained using likelihood based reward function and average of sum of correct outcomes based reward function. We evaluated the performance of the trained DRL agents using only likelihood as the the performance metric since likelihood is the expected value of the average of sum of correct outcomes."
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Experimental Setup (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Reward functions and performance metrics (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Training the LSTM (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Relation between rewards and thresholds (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Performance of RL agent when the number of items are varied (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Performance of TRPO vs. TNPG algorithms (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Performance of TRPO with reward shaping (research objective) (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Comparison between likelihood and average of sum of outcomes based reward functions (research objective) (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Evaluation (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)