Non-Stationary Reinforcement-Learning Based Dimensionality Reduction for Multi-objective Optimization of Wetland Design

This paper outlines a method of non-stationary reinforcement-based learning for feature selection. The method was developed for the Watershed REstoration using Spatio-Temporal Optimization of REsources (WRESTORE) system, which is a decision support system used for wetland design on the Eagle Creek Watershed, northwest of Indianapolis, Indiana. Our results show measurable impact for maximizing reward efficiently for the feature selection task. This work describes the existing WRESTORE system, provides an overview of related work in reinforcement learning and dimensionality reduction, and shows the impact of our work in the multi-objective optimization process of WRESTORE. The contribution of this work is the application of an RL-based feature selection technique in interactive optimization of watershed design.