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Internet of Vehicles (IoV) has been viewed as a pivotal innovation for laying out Intelligent Transportation Systems in shrewd urban communities and is perhaps of the most encouraging application later on Internet of Things. the 6th era has started to arise. With the appearance of 6G interchanges, tremendous organization frameworks, be generally utilized, and the amount of organization hubs will rise remarkable development, which brings about extremely high energy utilization. As perhaps of the most encouraging application in future Internet of Things, Internet of Vehicles (IoV) has been recognized as an essential innovation for fostering the Clever Transportation Frameworks in brilliant urban areas. With the rise of the sixth generation (6G) correspondences innovations, monstrous network frameworks will be thickly sent and the quantity of network hubs will increment dramatically, leading to very high energy utilization. There has been an upsurge important to foster the green IoV towards maintainable vehicular correspondence and networking in the 6G time. Be that as it may, as an extraordinary versatile ad-hoc network, the energy cost in an IoV framework includes the correspondence and calculation energy in addition to the fuel utilization and the power cost of moving vehicles. Besides, the energy collecting innovation, which is probably going to be adopted generally in 6G frameworks, will entangle the improvement of energy proficiency in the whole framework.A change in the 6G period, there has been an expansion in interest in fostering the green IoV for supportable vehicle correspondence and systems administration. Nonetheless, as a remarkable versatile impromptu organization, an IoV framework's energy cost incorporates energy for correspondence and figuring. Reinforcement Learning (RL) can successfully address issues with decision-making to accomplish this goal. Large-scale wireless networks have enormous and complex state and action spaces, though. As a result, RL might not be able to identify the ideal plan of action in a timely manner. To solve this problem, Deep Reinforcement Learning (DRL), a infusion of RL and DL, has been developed.