  We consider large-scale Markov decision processes (MDPs) with parameter
uncertainty, under the robust MDP paradigm. Previous studies showed that robust
MDPs, based on a minimax approach to handle uncertainty, can be solved using
dynamic programming for small to medium sized problems. However, due to the
"curse of dimensionality", MDPs that model real-life problems are typically
prohibitively large for such approaches. In this work we employ a reinforcement
learning approach to tackle this planning problem: we develop a robust
approximate dynamic programming method based on a projected fixed point
equation to approximately solve large scale robust MDPs. We show that the
proposed method provably succeeds under certain technical conditions, and
demonstrate its effectiveness through simulation of an option pricing problem.
To the best of our knowledge, this is the first attempt to scale up the robust
MDPs paradigm.
