Provably Efficient Exploration for RL with Unsupervised Learning

15 Mar 2020Fei FengRuosong WangWotao YinSimon S. DuLin F. Yang

We study how to use unsupervised learning for efficient exploration in reinforcement learning with rich observations generated from a small number of latent states. We present a novel algorithmic framework that is built upon two components: an unsupervised learning algorithm and a no-regret reinforcement learning algorithm... (read more)

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