###### Abstract
It was summarized that the sizing methods based on AI and multi-objective design are one of the most potent and powerful tools [3]. In this project, the Hybrid Optimization Model for Multiple Energy Resources (HOMER) was used for designing and determining the optimal sizing of the HRES for the case study due to it being user-friendly and easily implemented. The control strategy of HRES is necessary for improving the productivity and reliable operation of a power system working under the uncertainties of the RE resources and the dynamic loads. Under this system, maximum power point tracking (MPPT) control [10] is used to improve the conversion efficiency of solar and wind energy systems while an energy management system (EMS) [11] is developed for controlling the power flows among system components and handling reliable operation. The recent development and achievement in the fields of machine learning (ML) and reinforcement learning (RL) leads to new challenges and opportunities for energy management and MPPT control. RL-based control systems can learn and act just following experiences when interacting with the environment [12; 13]. In contrast, traditional methods need particular mathematical models of the system and environment, which requires highly control knowledge, data, and domain expertise. ML can be classified into three categories: supervised learning (task-driven, estimate next value), unsupervised (data-driven, determine clusters), and reinforcement learning (learning from trial and error) [13]. The RL controller can be considered as an agent, and the agent can learn how to act based on the reward and current states received from the environment [12]. Due to the potential development of ML in various areas, several researchers have switched their interest in the application of ML towards the control strategies of HRES, which will be discussed in the following sections. This study aims to generally present the overall process of energy system development for an isolated microgrid, especially for a hybrid renewable hydrogen energy system involving optimal sizing, EMS, and an MPPT controller. Firstly, HOMER software was used for the optimal sizing of HRES based on the actual load demand and weather data in Basco Island, Bantanes, Philippines as the case study. Next, a brief review of EMS and MPPT control based on the ML and RL techniques was conducted. Finally, a new hybrid method for MPPT control, which integrates the Q-learningand perturbation and observation (P&O) methods, was proposed to improve system performance. P&O is the mostly preferred algorithm for MPPT control [14; 15]. The major advantages of this method are simple structure and ease of implementation. But P&O turned out to be ineffective under the fast change of the temperature and irradiation, as well as the partial shading conditions. A large step size of the P&O duty cycle (D) provides fast convergence with poor tracking while the low step size duty cycle provides low convergence with the ability to reduce the oscillation at the maximum power point [15]. The reinforcement learning approach to solve the MPPT problem aims to learn the system behavior based on the PV source response. The RL-based MPPT controller monitors the environmental state of the PV source and uses different step sizes of the duty cycle to adjust the perturbation to the operating voltage in achieving the maximum power. In references [16; 17], the authors present the good simulation results of reinforcement learning. In addition, reference [18] has presented considerable results towards a universal MPPT control method. It has also mentioned the potential future research on this topic including state-space reduction, RL algorithm optimization, a comparison between different RL algorithms, a more efficient optimal procedure, and the practical experiments. As discussed, we aim to combine the Q-learning with the P&O method to reduce the state space for the learning process and to enhance the good characteristics of the P&O controller in this study.