EAQR: A Multiagent Q-Learning Algorithm for Coordination of Multiple Agents
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-28
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
We propose a cooperative multiagent Q-learning algorithm called exploring actions according to Q-value ratios (EAQR).
Our aim is to design a multiagent reinforcement learning algorithm for cooperative tasks where multiple agents need to coordinate their behavior to achieve the best system performance.
In EAQR, Q-value represents the probability of getting the maximal reward, while each action is selected according to the ratio of its Q-value to the sum of all actions’ Q-value and the exploration rate ε.
Seven cooperative repeated games are used as cases to study the dynamics of EAQR.
Theoretical analyses show that in some cases the optimal joint strategies correspond to the stable critical points of EAQR.
Moreover, comparison experiments on stochastic games with finite steps are conducted.
One is the box-pushing, and the other is the distributed sensor network problem.
Experimental results show that EAQR outperforms the other algorithms in the box-pushing problem and achieves the theoretical optimal performance in the distributed sensor network problem.
American Psychological Association (APA)
Zhang, Zhen& Wang, Dongqing. 2018. EAQR: A Multiagent Q-Learning Algorithm for Coordination of Multiple Agents. Complexity،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1135662
Modern Language Association (MLA)
Zhang, Zhen& Wang, Dongqing. EAQR: A Multiagent Q-Learning Algorithm for Coordination of Multiple Agents. Complexity No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1135662
American Medical Association (AMA)
Zhang, Zhen& Wang, Dongqing. EAQR: A Multiagent Q-Learning Algorithm for Coordination of Multiple Agents. Complexity. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1135662
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135662