1. Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200438; 2. School of Computer Science, Fudan University, Shanghai 200438; 3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433
Abstract：Combined with deep models, deep reinforcement learning(RL) is widely applied in various fields such as intelligent control and game competition. However, the existing RL surveys mainly focus on some core difficulty and neglect the analysis of problem itself from an overall perspective. The practical application in real-world scenarios is confronted with many technical challenges , and the technical approaches for a particular problem are not as good as expected for specific scenarios. Therefore, problem setting is defined in this paper from six major aspects, including agent, task distribution, Markov decision process, policy class, learning objective and interaction mode. A problem setting-driven perspective is utilized to analyze overall research status, elementary and extended RL setting. Then, development direction, key technologies and main motivation of the current deep RL are further discussed. Moreover, expert interaction is taken as an example to further analyze the development trends of the field in general from the problem setting-driven perspective. Finally, hot topics and future directions for the field are proposed.
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