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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Machine Learning Street Talk (MLST)

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Unpacking Contrastive Learning in Reinforcement Learning

This chapter explores the intricacies of model-free and model-based reinforcement learning, with a spotlight on contrastive learning's role in unsupervised tasks. The speakers discuss the balance of loss functions, the significance of data augmentation, and the advantages of modern self-supervised models over traditional supervised approaches. By examining various applications and challenges across image and language domains, the chapter underscores the evolving landscape of reinforcement learning and its interaction with advanced learning paradigms.

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