Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Matthew Robbins, PhD
Abstract
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out of three environments while additionally providing easily interpretable decision tree policies. However, the proffered approach faces challenges in solving the LunarLander environment, indicating limitations in its current ability to scale to larger environments.
AFIT Designator
AFIT-ENS-MS-24-M-096
Recommended Citation
Rae, Victor R., "A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence" (2024). Theses and Dissertations. 7729.
https://scholar.afit.edu/etd/7729
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.