Optimal chaos control through reinforcement learning

被引：32

作者：

Gadaleta, S ^{[1
]}

Dangelmayr, G ^{[1
]}

机构：

[1] Colorado State Univ, Dept Math, Ft Collins, CO 80523 USA

来源：

CHAOS | 1999年 / 9卷 / 03期

关键词：

D O I：

10.1063/1.166451

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A general purpose chaos control algorithm based on reinforcement learning is introduced and applied to the stabilization of unstable periodic orbits in various chaotic systems and to the targeting problem. The algorithm does not require any information about the dynamical system nor about the location of periodic orbits. Numerical tests demonstrate good and fast performance under noisy and nonstationary conditions. (C) 1999 American Institute of Physics. [S1054-1500(99)00703-X].

引用

页码：775 / 788

页数：14

共 50 条

[41] Optimal Control via Reinforcement Learning with Symbolic Policy Approximation
Kubalik, Jiri
Alibekov, Eduard
Babuska, Robert
IFAC PAPERSONLINE, 2017, 50 (01): : 4162 - 4167
[42] Reinforcement Learning for Optimal Control of a District Cooling Energy Plant
Guo, Zhong
Coffman, Austin R.
Barooah, Prabir
2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 3329 - 3334
[43] Exploring optimal control of epidemic spread using reinforcement learning
Abu Quwsar Ohi
M. F. Mridha
Muhammad Mostafa Monowar
Md. Abdul Hamid
Scientific Reports, 10
[44] Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
Lawrence, Nathan P.
Stewart, Gregory E.
Loewen, Philip D.
Forbes, Michael G.
Backstrom, Johan U.
Gopaluni, R. Bhushan
IFAC PAPERSONLINE, 2020, 53 (02): : 243 - 248
[45] Exploring optimal control of epidemic spread using reinforcement learning
Ohi, Abu Quwsar
Mridha, M. F.
Monowar, Muhammad Mostafa
Hamid, Md. Abdul
SCIENTIFIC REPORTS, 2020, 10 (01)
[46] LOW-THRUST OPTIMAL CONTROL VIA REINFORCEMENT LEARNING
Miller, Daniel
Linares, Richard
SPACEFLIGHT MECHANICS 2019, VOL 168, PTS I-IV, 2019, 168 : 1817 - 1834
[47] Universal quantum control through deep reinforcement learning
Niu, Murphy Yuezhen
Boixo, Sergio
Smelyanskiy, Vadim N.
Neven, Hartmut
NPJ QUANTUM INFORMATION, 2019, 5 (1)
[48] Production flow control through the use of reinforcement learning
Silva, Tome
Azevedo, Americo
29TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING (FAIM 2019): BEYOND INDUSTRY 4.0: INDUSTRIAL ADVANCES, ENGINEERING EDUCATION AND INTELLIGENT MANUFACTURING, 2019, 38 : 194 - 202
[49] Universal quantum control through deep reinforcement learning
Murphy Yuezhen Niu
Sergio Boixo
Vadim N. Smelyanskiy
Hartmut Neven
npj Quantum Information, 5
[50] Optimal fluctuations and the control of chaos
Luchinsky, DG
Beri, S
Mannella, R
McClintock, PVE
Khovanov, IA
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2002, 12 (03): : 583 - 604

← 1 2 3 4 5 →