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A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Christoph Dann · Mehryar Mohri · Tong Zhang · Julian Zimmert

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @

Thompson Sampling is one of the most effective methods for contextual bandits and has been generalized to posterior sampling for certain MDP settings. However, existing posterior sampling methods for reinforcement learning are limited by being model-based or lack worst-case theoretical guarantees beyond linear MDPs. This paper proposes a new model-free formulation of posterior sampling that applies to more general episodic reinforcement learning problems with theoretical guarantees. We introduce novel proof techniques to show that under suitable conditions, the worst-case regret of our posterior sampling method matches the best known results of optimization based methods. In the linear MDP setting with dimension, the regret of our algorithm scales linearly with the dimension as compared to a quadratic dependence of the existing posterior sampling-based exploration algorithms.

Author Information

Christoph Dann (Google Research)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Tong Zhang (The Hong Kong University of Science and Technology)
Julian Zimmert (Google Research)

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