Factoring Exogenous State for Model-Free Monte Carlo
This addresses a scalability problem for policy analysts in domains like wildfire management, but it is incremental as it builds on existing MFMC methods.
The paper tackles the challenge of visualizing policies in high-dimensional Markov Decision Processes (MDPs) with expensive simulators by introducing MFMCi, a method that factors out state and action variables to enable Model-Free Monte Carlo (MFMC) to work effectively, achieving improved performance in a wildfire management MDP.
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.