東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Topics in Convex Optimization for Optimal Control and Reinforcement Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Topics in Convex Optimization for Optimal Control and Reinforcement Learning./
Author:	Kim, Jong.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	97 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: A.
Contained By:	Dissertations Abstracts International83-05A.
Subject:	Heat transfer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688307
ISBN:	9798544204114

Topics in Convex Optimization for Optimal Control and Reinforcement Learning.
Kim, Jong.

Topics in Convex Optimization for Optimal Control and Reinforcement Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 97 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: A.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Optimal control describes the problem of finding a control to minimize an objective function for a dynamical system over a period of time. It has been applied in a wide variety of applications in robotics, signal processing, and machine learning over several decades. On the other hand, reinforcement learning (RL) assumes the uncertain model of transition dynamics and has achieved great empirical success in recent years including simulated game scenarios and robotics. This thesis focuses on topics in both optimal control and reinforcement learning.

ISBN: 9798544204114Subjects--Topical Terms:

3391367
Heat transfer.

Topics in Convex Optimization for Optimal Control and Reinforcement Learning.
LDR:01725nmm a2200385 4500 001 2344860
005 20220531062159.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798544204114
035 $a (MiAaPQ)AAI28688307
035 $a (MiAaPQ)STANFORDth484mz3417
035 $a AAI28688307
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kim, Jong. $3 3683685
245 1 0 $a Topics in Convex Optimization for Optimal Control and Reinforcement Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 97 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: A.
500 $a Advisor: Boyd, Stephen; Lall, Sanjay; Pilanci, Mert.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Optimal control describes the problem of finding a control to minimize an objective function for a dynamical system over a period of time. It has been applied in a wide variety of applications in robotics, signal processing, and machine learning over several decades. On the other hand, reinforcement learning (RL) assumes the uncertain model of transition dynamics and has achieved great empirical success in recent years including simulated game scenarios and robotics. This thesis focuses on topics in both optimal control and reinforcement learning.
590 $a School code: 0212.
650 4 $a Heat transfer. $3 3391367
650 4 $a Electrolytes. $3 656992
650 4 $a Artificial intelligence. $3 516317
650 4 $a Aging. $3 543123
650 4 $a Optimization. $3 891104
650 4 $a Automobiles. $3 560291
650 4 $a Convex analysis. $3 3681761
650 4 $a Hybrid vehicles. $3 3683686
650 4 $a Energy. $3 876794
650 4 $a Robotics. $3 519753
650 4 $a Mathematics. $3 515831
650 4 $a Gerontology. $3 533633
650 4 $a Medicine. $3 641104
650 4 $a Transportation. $3 555912
690 $a 0771
690 $a 0493
690 $a 0791
690 $a 0800
690 $a 0405
690 $a 0351
690 $a 0564
690 $a 0709
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05A.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688307