Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. (arXiv:2206.07659v1 [cs.LG])

We propose a general framework to design posterior sampling methods for
model-based RL. We show that the proposed algorithms can be analyzed by
reducing regret to Hellinger distance based conditional probability estimation.
We further show that optimistic posterior sampling can control this Hellinger
distance, when we measure model error via data likelihood. This technique
allows us to design and analyze unified posterior sampling algorithms with
state-of-the-art sample complexity guarantees for many model-based RL settings.
We illustrate our general result in …

arxiv complexity lg posterior rl sampling