Application of Reinforcement Learning in Multiagent Intelligent Decision-Making.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
10
08
2022
revised:
27
08
2022
accepted:
30
08
2022
entrez:
26
9
2022
pubmed:
27
9
2022
medline:
28
9
2022
Statut:
epublish
Résumé
The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a new concept in the theory of gaming. In some game issues that Nash equilibrium was not the optimal solution, the regret minimization had better performance. Herein, we introduce the regret minimization into multiagent reinforcement learning and propose a multiagent regret minimum algorithm. This chapter first introduces the Nash Q-learning algorithm and uses the overall framework of Nash Q-learning to minimize regrets into the multiagent reinforcement learning and then verify the effectiveness of the algorithm in the experiment.
Identifiants
pubmed: 36156954
doi: 10.1155/2022/8683616
pmc: PMC9507689
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8683616Informations de copyright
Copyright © 2022 Xiaoyu Han.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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