# Factoring Exogenous State for Model-Free Monte Carlo

arXiv: https://arxiv.org/abs/1703.09390 · HTML: https://mlsanity.com/paper/1703.09390/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning](https://mlsanity.com/paper/2209.10974/) (2022)
- [Reinforcement Learning with Exogenous States and Rewards](https://mlsanity.com/paper/2303.12957/) (2023)
- [Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning](https://mlsanity.com/paper/2111.08066/) (2021)

## Abstract (arXiv)

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.