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This article discusses the use of personalized recommender systems (RSs) in online learning platforms to improve the learning experience for students. The proposed framework integrates deep reinforcement learning and multi-agent approaches to personalize course recommendations and learning paths based on factors such as learner sentiments, learning style, and competency. The results of experiments on a MOOC dataset demonstrate the superiority of this approach over baseline models.