Model-Based Bayesian Reinforcement Learning in Large Structured Domains
  Model-based Bayesian reinforcement learning has generated significant
interest in the AI community as it provides an elegant solution to the optimal
exploration-exploitation tradeoff in classical reinforcement learning.
Unfortunately, the applicability of this type of approach has been limited to
small domains due to the high complexity of reasoning about the joint posterior
over model parameters. In this paper, we consider the use of factored
representations combined with online planning techniques, to improve
scalability of these methods. The main contribution of this paper is a Bayesian
framework for learning the structure and parameters of a dynamical system,
while also simultaneously planning a (near-)optimal sequence of actions.
