  We study reinforcement learning in non-episodic factored Markov decision
processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms
for FMDPs. Assuming oracle access to an FMDP planner, they enjoy a Bayesian and
a frequentist regret bound respectively, both of which reduce to the
near-optimal bound $\widetilde{O}(DS\sqrt{AT})$ for standard non-factored MDPs.
We propose a tighter connectivity measure, factored span, for FMDPs and prove a
lower bound that depends on the factored span rather than the diameter $D$. In
order to decrease the gap between lower and upper bounds, we propose an
adaptation of the REGAL.C algorithm whose regret bound depends on the factored
span. Our oracle-efficient algorithms outperform previously proposed
near-optimal algorithms on computer network administration simulations.

Machine Learning;Machine Learning