  Policy analysts wish to visualize a range of policies for large
simulator-defined Markov Decision Processes (MDPs). One visualization approach
is to invoke the simulator to generate on-policy trajectories and then
visualize those trajectories. When the simulator is expensive, this is not
practical, and some method is required for generating trajectories for new
policies without invoking the simulator. The method of Model-Free Monte Carlo
(MFMC) can do this by stitching together state transitions for a new policy
based on previously-sampled trajectories from other policies. This "off-policy
Monte Carlo simulation" method works well when the state space has low
dimension but fails as the dimension grows. This paper describes a method for
factoring out some of the state and action variables so that MFMC can work in
high-dimensional MDPs. The new method, MFMCi, is evaluated on a very
challenging wildfire management MDP.
