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Nearly every book on reinforcement learning contains a chapter that explains the differences between model-free and model-based reinforcement learning.
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Also, model-based learning applies to problems with data traits such as real-time data, heterogeneous data, unlabeled data and data with missing parts, and data collected with known biases.