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

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

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...

Full description

Bibliographic Details
Main Authors: Cao, Kun, Xie, Lihua
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Electrical and electronic engineering
Maximum Principle
Multistage Game
Online Access:https://hdl.handle.net/10356/162585

Internet

https://hdl.handle.net/10356/162585

Similar Items