Measuring the Performance of Multi-Objective Reinforcement Learning algorithms - Nile River Case Study
J. Kontak (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Z. Osika – Mentor (TU Delft - Policy Analysis)
Pradeep Murukannaiah – Mentor (TU Delft - Interactive Intelligence)
L. Cruz – Graduation committee member (TU Delft - Software Engineering)
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 study investigates the use of Multi-Objective Natural Evolution Strategies (MONES) to optimise water management control policies in the Nile River Basin, focusing on four key objectives: minimising irrigation deficits for Egypt and Sudan, maximising hydropower production for Ethiopia, and maintaining water level in the High Aswan Dam (HAD). The developed Nile River simulation was integrated with MONES and used to train an agent for making release decisions. Performance metrics including hypervolume, epsilon-indicator, and Inverted Generational Distance Plus (IGD+) were employed to compare MONES with the EMODPS baseline. The results indicate that while MONES identifies feasible solutions, it falls short in exploration and overall performance compared to EMODPS. The study contributes to the development of water management strategies by open-sourcing Nile River framework compatible with reinforcement learning and demonstrating the potential and limitations of MONES in multi-objective optimisations.