PAC-Bayesian Policy Evaluation for Reinforcement Learning

Understand

Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks.

• The correctness of the Bayesian analysis and inference, however, largely depends on accuracy and correctness of these priors.
• PAC-Bayesian methods overcome this problem by providing bounds that hold regardless of the correctness of the prior distribution.
• This paper introduces the first PAC-Bayesian bound for the batch reinforcement learning problem with function approximation.