  Bayesian approaches provide a principled solution to the
exploration-exploitation trade-off in Reinforcement Learning. Typical
approaches, however, either assume a fully observable environment or scale
poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a
framework that is able to exploit the underlying structure while learning the
dynamics in partially observable systems. We also present a belief tracking
method to approximate the joint posterior over state and model variables, and
an adaptation of the Monte-Carlo Tree Search solution method, which together
are capable of solving the underlying problem near-optimally. Our method is
able to learn efficiently given a known factorization or also learn the
factorization and the model parameters at the same time. We demonstrate that
this approach is able to outperform current methods and tackle problems that
were previously infeasible.
