Reinforcement learning has always been a research hotspot in the machine learning community, which aims to model the process of investigating the interaction between agents and the environment, making sequential decisions, optimizing strategies, and maximizing cumulative returns. With the rapid development of artificial intelligence technology, the huge research value and application potential of reinforcement learning have gradually become prominent. In this paper, we first introduce the development background of reinforcement learning, and then introduce the application of reinforcement learning from three the aspects of games, finance, and autonomous driving. Regarding the game field, we introduce the methods and results of reinforcement learning in Atari 2600 games and strategy games. For the financial field, we show the application of reinforcement learning in the financial field from the perspective of stock value prediction and deep hedging of derivatives. When it comes to the autonomous driving, we briefly describe the process of automatic driving and show how reinforcement learning constructs strategy function in the field of automatic driving from the perspectives of automatic parking and road form. Finally, we look forward to the algorithms and applications of reinforcement learning, and give suggestions for some future research directions.

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