DocumentCode
1648093
Title
Reinforcement Strategy Using Quantum Amplitude Amplification for Robot Learning
Author
Daoyi, Dong ; Chunlin, Chen ; Hanxiong, Li
Author_Institution
Chinese Acad. of Sci., Beijing
fYear
2007
Firstpage
571
Lastpage
575
Abstract
Quantum amplitude amplification is a kind of useful technique in quantum computation and it can boost the success probability of some quantum algorithms. Reinforcement strategy in reinforcement learning is essentially to boost the selection probability of "good" action. Considering the common characteristics, this paper uses the idea of amplitude amplification to reinforcement learning as a new reinforcement strategy, proposes a learning algorithm based on quantum amplitude amplification and demonstrates its effectiveness through simulated experiments.
Keywords
learning (artificial intelligence); probability; quantum computing; robots; quantum algorithm; quantum amplitude amplification; quantum computation; reinforcement learning; robot learning; selection probability; success probability; Artificial intelligence; Control systems; Fuzzy logic; Information processing; Intelligent robots; Learning; Mobile robots; Quantum computing; Quantum mechanics; Robot sensing systems; Quantum Amplitude Amplification; Reinforcement Learning; Reinforcement Strategy; Robot Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference, 2007. CCC 2007. Chinese
Conference_Location
Hunan
Print_ISBN
978-7-81124-055-9
Electronic_ISBN
978-7-900719-22-5
Type
conf
DOI
10.1109/CHICC.2006.4347206
Filename
4347206
Link To Document