Linear inverse reinforcement learning in continuous time and space

被引：0
|
作者：
Kamalapurkar, Rushikesh [1 ]
机构：
[1] Oklahoma State Univ, Sch Mech & Aerosp Engn, Stillwater, OK 74078 USA
来源：
2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC) | 2018年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper develops a data-driven inverse reinforcement learning technique for a class of linear systems to estimate the cost function of an agent online, using input-output measurements. A simultaneous state and parameter estimator is utilized to facilitate output-feedback inverse reinforcement learning, and cost function estimation is achieved up to multiplication by a constant.
引用
收藏
页码：1683 / 1688
页数：6
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