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

• المؤلفون الرئيسيون: Cao, Kun, Xie, Lihua
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Electrical and electronic engineering; Maximum Principle; Multistage Game
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/162585
• المؤسسة: Nanyang Technological University

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...