Reinforcement Learning Enhanced New Energy Vehicle Dynamic Subsidy Strategies

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Reinforcement Learning Enhanced New Energy Vehicle Dynamic Subsidy Strategies

Zhou, Guilin / Wang, Jie / Liao, Yuhong / Zhan, Zhenfei

In recent years, game theory and reinforcement learning have become very popular research fields in today's society. As the most strategic analysis and optimization research method, they can be used in the study of subsidy strategy of China's new energy automobile industry to solve the problems caused by the government's subsidy of new energy vehicles. This paper studies the evaluation methods and strategy optimization methods of government subsidy strategies in different situations, and applies them to the subsidy strategies and other strategy optimization problems of new energy vehicles in China. Firstly, based on game theory, this paper studies the evaluation method of government subsidy strategy in the case of “double reciprocity” and “one strong and one weak” by constructing the game process of “double reciprocity” enterprises and “one strong and one weak” enterprises. Then, taking the new energy vehicle market as the background, this paper studies the penetration model of the new energy vehicle market, puts forward the dynamic game model between the government and new energy vehicle enterprises in the new energy vehicle market, and studies the optimization method of government subsidy strategy by combining the government enterprise dynamic game model and reinforcement learning model. Then, using the proposed government subsidy strategy evaluation method and subsidy strategy optimization method, this paper studies the effectiveness of China's new energy vehicle subsidy policy and the Optimal Subsidy strategy of new energy vehicles. Finally, the applicability and validity of the proposed strategy is demonstrated.