Using Q-learning and genetic algorithms to improve the efficiency of weight adjustments for optimal control and design problems

Journal of Computing and Information Science in Engineering

Volumn 7, Issue 4, 2007, Pages 302-308

  Kamall, Kaivan ^a   Jiang, L J ^b   Yen, John ^a   Wang, K W ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b The Pennsylvania State University   (United States)

Author keywords

Genetic algorithms; Optimal control; Q learning; Weight selection

Indexed keywords

ADAPTIVE STRUCTURES; COMPLEX DESIGN PROBLEMS; COMPUTATIONAL COSTS; CONTROL EFFORTS; CONTROL GAINS; CONVERGENCE RATES; DESIGN PARAMETERS; DESIGN PROBLEMS; NUMERICAL EXPERIMENTS; OPTIMAL CONTROL; Q-LEARNING; REINFORCEMENT LEARNING TECHNIQUES; SELECTION PROCESS; SIMILAR DESIGNS; SPEED-UP; VIBRATION CONTROL PROBLEMS; WEIGHT SELECTION; WEIGHTING MATRICES;

ADAPTIVE CONTROL SYSTEMS; COST FUNCTIONS; DESIGN; EDUCATION; GENETIC ALGORITHMS; GENETIC ENGINEERING; OPTIMAL CONTROL SYSTEMS; OPTIMIZATION; REINFORCEMENT; VIBRATION CONTROL; WEIGHING;

LEARNING ALGORITHMS;

EID: 62349135588     PISSN: 15309827     EISSN: None     Source Type: Journal    
DOI: 10.1115/1.2739502     Document Type: Article

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