The rapid growth of electric vehicles (EVs) in recent years has been propelled by their eco-friendly benefits and expanding role within sustainable energy systems. In smart grids, EVs act as flexible electrical loads and a distributed energy storage unit. Using vehicle-to-grid (V2G) technology, they exchange power bidirectionally to enhance microgrid flexibility and increase renewable-energy utilization. Leveraging time-varying electricity prices and V2G capabilities enables microgrid operators to cut operational costs by adjusting EV charge-discharge schedules. However, designing these optimal schedules is challenging, as EV arrival and departure times, as well as electricity price trajectories, are highly unpredictable. To address this issue, the scheduling problem was modeled within a Markov Decision Process (MDP) framework, followed by the development of a deep reinforcement learning (DRL)-based solution. Specifically, an algorithm based on a Twin-delayed deterministic policy gradient (TD3) is implemented to autonomously derive optimal continuous charging and discharging decisions through real-time interactions with the environment. Extensive simulations demonstrate that the proposed DRL-based framework effectively reduces microgrid operational costs while ensuring EV owner satisfaction through State of Charge (SoC) requirements, optimal use of photovoltaic (PV) energy, and coordinated real-time V2G scheduling across all vehicles. © 2013 IEEE.