RL4Water: Climate-Resilient Water Management via Reinforcement Learning
Investigation of Different Visualization Techniques for the Multi-Objective Reinforcement Learning Results
B. Tezcan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Z. Osika – Mentor (TU Delft - Policy Analysis)
Pradeep Murukannaiah – Coach (TU Delft - Interactive Intelligence)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This paper studies the simulation of the Nile River as a multi-objective reinforcement learning problem. The main goal of this essay is to develop and evaluate the visualization techniques to effectively present the results of reinforcement learning models. Using a multi-objective approach, visualizations are very important for understanding the trade-offs and complexities in managing the Nile River problem.
This study includes a user evaluation to compare different visualizations, analyzing their effectiveness in terms of their clarity and usefulness using ANOVA test. Additionally, the effectiveness of clustering and full data points will be analyzed using a chi-square test to choose which visualisation technique works the best.
According to the results, stacked bar chart and parallel coordinates plot performed the best, while the spider plot performed the worst. Additionally, there is no preference between clustered and full data points visualizations based on the user evaluation.