DocumentCode
460758
Title
A Quantum Reinforcement Learning Method for Repeated Game Theory
Author
Chen, Chunlin ; Dong, Daoyi ; Shi, Qiong ; Yu Dong
Author_Institution
Dept. of Control & Syst. Eng., Nanjing Univ.
Volume
1
fYear
2006
fDate
Nov. 2006
Firstpage
68
Lastpage
72
Abstract
In this paper, a quantum reinforcement learning method is proposed for repeated game theory. First, the quantum reinforcement learning algorithm is introduced based on quantum state superposition principle and its superiority is analyzed. Then, it is applied to repeated games and the experiments show its effectiveness. Related issues are also discussed before the conclusion is given
Keywords
game theory; learning (artificial intelligence); quantum computing; quantum reinforcement learning; quantum state superposition principle; repeated game theory; Algorithm design and analysis; Artificial intelligence; Control systems; Databases; Game theory; History; Learning systems; Nuclear magnetic resonance; Quantum computing; Quantum mechanics;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security, 2006 International Conference on
Conference_Location
Guangzhou
Print_ISBN
1-4244-0605-6
Electronic_ISBN
1-4244-0605-6
Type
conf
DOI
10.1109/ICCIAS.2006.294092
Filename
4072045
Link To Document