“Bayesian Reinforcement Learning: A Survey”, 2016-09-14 (; backlinks; similar):
Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: (1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and (2) it provides a machinery to incorporate prior knowledge into the algorithms.
We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class.
The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.