Efficient model-based reinforcement learning for approximate online optimal control

被引：61

作者：

Kamalapurkar, Rushikesh ^{[1
]}

Rosenfeld, Joel A. ^{[2
]}

Dixon, Warren E. ^{[2
]}

机构：

[1] Oklahoma State Univ, Sch Mech & Aerosp Engn, Stillwater, OK 74078 USA

[2] Univ Florida, Dept Mech & Aerosp Engn, Gainesville, FL USA

来源：

AUTOMATICA | 2016年 / 74卷

基金：

美国国家科学基金会;

关键词：

Model-based reinforcement learning; Data-based control; Adaptive control; Local approximation; DISCRETE-TIME-SYSTEMS; ADAPTIVE OPTIMAL-CONTROL; NONLINEAR-SYSTEMS; NETWORK; DYNAMICS;

D O I：

10.1016/j.automatica.2016.08.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using a state following (StaF) kernel method to approximate the value function. Unlike traditional methods that aim to approximate a function over a large compact set, the StaF kernel method aims to approximate a function in a small neighborhood of a state that travels within a compact set. Simulation results demonstrate that stability and approximate optimality of the control system can be achieved with significantly fewer basis functions than may be required for global approximation methods. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：247 / 258

页数：12

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