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Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
[article]
2022
arXiv
pre-print
In view of its power in extracting feature representation, contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL), leading to efficient policy learning in various applications. Despite its tremendous empirical successes, the understanding of contrastive learning for RL remains elusive. To narrow such a gap, we study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov
arXiv:2207.14800v1
fatcat:eg555bavt5gb7ftahu4hhdbobi