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Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Shuang Qiu · Lingxiao Wang · Chenjia Bai · Zhuoran Yang · Zhaoran Wang

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #822

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 on various applications. Despite its tremendous empirical successes, the understanding of contrastive learning for RL remains elusive. To narrow such a gap, we study contrastive-learning empowered RL for a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. For both models, we propose to extract the correct feature representations of the low-rank model by minimizing a contrastive loss. Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs. We further theoretically prove that our algorithm recovers the true representations and simultaneously achieves sample efficiency in learning the optimal policy and Nash equilibrium in MDPs and MGs. We also provide empirical studies to demonstrate the efficacy of our algorithm. To the best of our knowledge, we provide the first provably efficient online RL algorithm that incorporates contrastive learning for representation learning.

Author Information

Shuang Qiu (University of Chicago)
Lingxiao Wang (Northwestern University)
Chenjia Bai (Harbin Institute of Technology)
Zhuoran Yang (Yale University)
Zhaoran Wang (Northwestern University)

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