This project aimed to investigate reinforcement learning (RL) algorithms to improve water management policy development in the Nile Basin, with a focus on the Multi-Objective Natural Evolution Strategies (MONES) and Evolutionary Multi-Objective Direct Policy Search (EMODPS) algorithms. This project intended to refactor a Nile Basin simulation to be compatible with the MONES algorithm, which continues the exploration of different machine learning algorithms in water resource management. Additionally, the RL algorithms were aimed at training using two climate data sets: human-favourable and climate-varying conditions, and then evaluating on the satisfaction and regret metrics. The successful integration of the MONES framework shows the feasibility of utilizing advanced RL algorithms for water management problems. Initial results indicate that the MONES algorithm underperforms compared to the EMODPS algorithm according to hypervolume and diversity of solutions, however, further research is needed to test whether this claim holds. The EMODPS algorithm faced challenges in finding optimal solutions when dealing with variable climate conditions scenarios, accentuating the need for robust solutions, which consider a variety of possible climate outcomes. The observed sensitivity to variable climate conditions underlines the crucial importance of accurate and recent data, as well as the need to consider the climate change effects on water management. The study concluded with suggestions that future simulations of water management strategies may be improved if a broader set of external factors and a more realistic representation of objectives are included in the simulation model. These improvements stand to positively impact the accuracy, applicability and reliability of future simulations.