Abstract: Reinforcement learning and generative adversarial networks are hot research topics in the field of artificial intelligence in recent years, and have achieved excellent performance in many fields. The combination of the two fields has begun to appear in the recent future, which makes the interactive learning characteristics of reinforcement learning and the heuristics of generative adversarial networks improve each other. This paper first summarizes the theory of reinforcement learning and generative adversarial networks. Secondly, this paper elaborates on the latest research directions of using reinforcement learning to improve generative adversarial networks, and using generative adversarial networks to improve reinforcement learning. Finally, this paper summarizes the successful application in the field of natural language and machine control, and gives the future development trend.

Key words: reinforcement learning, generative adversarial networks, deep learning, artificial intelligence

摘要： 强化学习和生成式对抗网络是近年来人工智能领域的两个热门主题，在众多领域表现非常出色。近期出现较多关于两者结合的工作与报道，将强化学习交互式学习的优点与生成式对抗网络的启发自博弈思想相互融合。对两者结合的最新进展进行了梳理、比较与实验分析。对强化学习与生成式对抗网络的理论进行了概述；从强化学习改进生成式对抗网络、生成式对抗网络改进强化学习两个研究方向进行了阐述与比较，通过实验方式分析了这些方法在自然语言、机器控制领域的应用情况；展望了可能的发展趋势。

关键词: 强化学习, 生成式对抗网络, 深度学习, 人工智能