Multi-agent reinforcement learning based on quantum andant colony algorithm theory

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 learnin...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1759 - 1764
Main Authors Jingweijia Tan, Xiang-Ping Meng, Tong Wang, Sheng-Bin Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Ant Colony Algorithm
Cognitive science
Collaboration
Computer science
Cybernetics
Information technology
Machine learning
Machine learning algorithms
Multiagent systems
Q-Learning
Quantum Algorithm
Quantum computing
Quantum mechanics
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Summary: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.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212291