Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. (arXiv:2207.14800v1 [cs.LG])

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 games (MGs) with low-rank
transitions. For both models, we …

arxiv learning lg online reinforcement learning reinforcement reinforcement learning self-supervised learning supervised learning