With the research and development of deep reinforcement learning,the application of reinforcement learning(RL)in real-world problems such as game and optimization decision,and intelligent driving has also made significant progress.However,reinforcement learning has difficulty in manually designing the reward function in the interaction between an agent and its environment,so researchers have proposed the research direction of inverse re-inforcement learning(IRL).How to learn reward functions from expert demonstrations and perform strategy optim-ization is a novel and important research topic with very important research implications in the field of artificial in-telligence.This paper presents a comprehensive overview of the recent progress of inverse reinforcement learning al-gorithms.Firstly,new advances in the theory of inverse reinforcement learning are introduced,then the challenges faced by inverse reinforcement learning and the future development trends are analyzed,and finally the progress and application prospects of inverse reinforcement learning are discussed.
维普
万方数据