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.
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;
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
DOI :
10.1109/ICCIAS.2006.294092
