As inverter based renewable energy increases, modern power systems are faced with reduced inertia which makes it a major source of concern in the frequency stability and the momentary performance. Inverters based on gridforming inverter configurations with virtual inertia control have become a valid solution, and reinforcement learning is an adaptive optimization of control parameters, which operates in changing conditions. The control strategies-based on reinforcement learning of virtual-inertia enabled grid-connected inverters are examined in this review with focus on modelling technique, control design, and stability behavior.