MULTI-AGENT REINFORCEMENT LEARNING BASED ON QUANTUM ANDANT COLONY ALGORITHM THEORY

被引：1

作者：

Tan, Jingweijia ^{[1
]}

Meng, Xiang-Ping ^{[2
]}

Wang, Tong ^{[3
]}

Wang, Sheng-Bin ^{[3
]}

机构：

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China

[2] Changchun Inst Technol, Sch Elect Engn & Informat, Jilin, Peoples R China

[3] Northeast Dianli Univ, Sch Informat Engn, Jilin, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Quantum Algorithm; Ant Colony Algorithm; Q-Learning; MODEL;

D O I：

10.1109/ICMLC.2009.5212291

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a novel multi-agent reinforcement learning algorithm is proposed based on Q-Learning, ant colony algorithm and quantum algorithm. As in reinforcement learning algorithm, when the number of agents is large enough, all of the action selection methods will be failed: the speed of learning is decreased sharply. So, we try to combine the ant colony algorithm, quantum algorithm with Q-learning to resolve the above problem. At last, both the theory analysis and experiment result demonstrate that the improved Q-learning is feasible and very efficient.

引用

页码：1759 / +

页数：2

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