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This paper develops an adaptive control method for controlling frequency and voltage of an islanded mini/micro grid (M/μG) using reinforcement learning method. Reinforcement learning (RL) is one of the branches of the machine learning, which is the main solution method of Markov decision process (MDPs). Among the several solution methods of RL, the Q-learning method is used for solving RL in this paper because it is a model-free strategy and has a simple structure. The proposed control mechanism is consisting of two main parts. The first part is a classical PID controller that is fixed tuned using Salp swarm algorithm (SSA). The second part is a Q-learning based control strategy which is consistent and updates it's characteristics according to the changes in the system continuously. Eventually, the dynamic performance of the proposed control method is evaluated in a real M/μG compared to fuzzy PID and classical PID controllers. The considered M/μG is a part of Denmark distribution system which is consist of three combined heat and power (CHP) and three WTGs. Simulation results indicate that the proposed control strategy has an excellent dynamic response compared to both intelligent and traditional controllers for damping the voltage and frequency oscillations. Keyword: Q-learning, Mini/Microgrid, Voltage control, Frequency oscillation damping, Fuzzy PID controller. NOMENCLATURE Abbreviations AHC Adaptive heuristic critic AR Average reward CHP Combined heat and power DG Distributed generation GTG Gas turbine generators ISE Integral of squared error ITAE Integral of time multiplied by absolute error M/μG Mini/Micro-grid OS Overshoot PV Photovoltaic RL Reinforcement Learning SSA Salp Swarm Algorithm Ts Settling time US Undershoot

Mini/Micro-Grid Adaptive Voltage and Frequency Stability Enhancement Using Q-learning Mechanism