Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. In this survey, we systematically categorize the deep RL algorithms and applications, and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods, model-free methods, and advanced RL methods. We thoroughly analyze the advances including exploration, inverse RL, and transfer RL. Finally, we outline the current representative applications, and analyze four open problems for future research.

深度强化学习（RL）已成为人工智能研究中最受欢迎的主题之一。它已被广泛用于各种领域，例如端到端控制，机器人控制，推荐系统和自然语言对话系统。在本次调查中，我们对深度RL算法及其应用进行了系统分类，并通过将现有深度RL算法分为基于模型的方法，无模型方法和高级RL方法进行了详细综述。我们彻底分析了包括探索，逆RL和转移RL在内的进步。最后，我们概述了当前的代表性应用，并分析了四个未解决的问题，以供将来研究。