Heterogeneous multi-player imitation learning
Bosen Lian1
Wenqian Xue2
Frank L.Lewis3
1.Department of Electrical and Computer Engineering,Auburn University,Auburn,AL 36849,USA2.State Key Laboratory of Synthetical Automation for Process Industries and International Joint Research Laboratory of Integrated Automation,Northeastern University,Shenyang 110819,Liaoning,China3.UTA Research Institute,University of Texas at Arlington(UTA),Fort Worth,TX 76118,USA
摘要：This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics.We propose a model-free data-driven inverse reinforcement learning(RL)algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert's states and control inputs.This allows us to address the imitation learning problem without prior knowledge of the expert's system dynamics.To achieve this,we provide a basic model-based algorithm that is built upon RL and inverse optimal control.This serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function approximators.Theoretical analysis and simulation examples verify the methods.
机标关键词：learningheterogeneousimitationmulti-player
论文发表日期：2023-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 281-291 )
英文信息