Self-Supervised Reinforcement Learning with Contrastive Representations
princeton.edu
Self-Supervised Reinforcement Learning with Contrastive Representations
Chongyi Zheng, Benjamin EysenbachUnsupervised learning is really powerful. It lies at the heart of large language models (just predict the next token), generative image models (predict what noise to remove from an image), and vision-language models (which text matches with which image). In recent work, we have explored how to build unsupervised RL
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