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 :
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