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Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
Fei Feng · Ruosong Wang · Wotao Yin · Simon Du · Lin Yang

Mon Dec 07 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Reinforcement Learning
in Orals & Spotlights Track 04: Reinforcement Learning »

Motivated by the prevailing paradigm of using unsupervised learning for efficient exploration in reinforcement learning (RL) problems [tang2017exploration,bellemare2016unifying], we investigate when this paradigm is provably efficient. We study episodic Markov decision processes with rich observations generated from a small number of latent states. We present a general algorithmic framework that is built upon two components: an unsupervised learning algorithm and a no-regret tabular RL algorithm. Theoretically, we prove that as long as the unsupervised learning algorithm enjoys a polynomial sample complexity guarantee, we can find a near-optimal policy with sample complexity polynomial in the number of latent states, which is significantly smaller than the number of observations. Empirically, we instantiate our framework on a class of hard exploration problems to demonstrate the practicality of our theory.

Keywords: Gaussian Processes · Decision and Control · Probabilistic Methods · Reinforcement Learning and Planning
[ Paper ]

Author Information

Fei Feng (University of California, Los Angeles)
Ruosong Wang (Carnegie Mellon University)
Wotao Yin (Alibaba US, DAMO Academy)
Simon Du (Institute for Advanced Study)
Lin Yang (UCLA)

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