Game-theoretic inverse reinforcement learning: a differential pontryagin's maximum principle approach

• Main Authors: Cao, Kun, Xie, Lihua
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Electrical and electronic engineering; Maximum Principle; Multistage Game
• 在線閱讀: https://hdl.handle.net/10356/162585

This paper proposes a game-theoretic inverse reinforcement learning (GT-IRL) framework, which aims to learn the parameters in both the dynamic system and individual cost function of multistage games from demonstrated trajectories. Different from the probabilistic approaches in computer science commu...