Evolutionary game theory and multi-agent reinforcement learning

2005 ◽  
Vol 20 (1) ◽  
pp. 63-90 ◽  
Author(s):  
KARL TUYLS ◽  
ANN NOWÉ
Keyword(s):  
Game Theory ◽  
Reinforcement Learning ◽  
Evolutionary Game Theory ◽  
State Of The Art ◽  
Evolutionary Game ◽  
The State ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Mathematical Connection

In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory.

scholarly journals Dynamic Model of Collaboration in Multi-Agent System Based on Evolutionary Game Theory

Games ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 75
Author(s):  
Zhuozhuo Gou ◽  
Yansong Deng
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Multi Agent Systems ◽  
Exchange Of Information ◽  
Message Communication ◽  
Stable Conditions ◽  
Agent Collaboration ◽  
Multi Agent ◽  
Reducing Costs

Multi-agent collaboration is greatly important in order to reduce the frequency of errors in message communication and enhance the consistency of exchanging information. This study explores the process of evolutionary decision and stable strategies among multi-agent systems, including followers, leaders, and loners, involved in collaboration based on evolutionary game theory (EGT). The main elements that affected the strategies are discussed, and a 3D evolution model is established. The evolutionary stability strategy (ESS) and stable conditions were analyzed subsequently. Numerical simulation results were obtained through MATLAB simulation, and they manifested that leaders play an important role in exchanging information with other agents, accepting agents’ state information, and sending messages to agents. Then, with the positivity of receiving and feeding back messages for followers, implementing message communication is profitable for the system, and the high positivity can accelerate the exchange of information. At the behavior level, reducing costs can strengthen the punishment of impeding the exchange of information and improve the positivity of collaboration to facilitate the evolutionary convergence toward the ideal state. Finally, the EGT results revealed that the possibility of collaboration between loners and others is improved, and the rewards are increased, thereby promoting the implementation of message communication that encourages leaders to send all messages, improve the feedback positivity of followers, and reduce the hindering degree of loners.

scholarly journals Insights on Multi-Agent Systems Applications for Supply Chain Management

2020 ◽  
Vol 12 (5) ◽  
pp. 1935
Author(s):  
Roberto Dominguez ◽  
Salvatore Cannella
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
State Of The Art ◽  
Relevant Literature ◽  
The State ◽  
Multi Agent Systems ◽  
General Picture ◽  
Chain Management ◽  
Agent Systems ◽  
Multi Agent

In this paper, we review relevant literature on the development of multi-agent systems applications for supply chain management. We give a general picture of the state of the art, showing the main applications developed using this novel methodology for analyzing diverse problems in industry. We also analyze generic frameworks for supply chain modelling, showing their main characteristics. We discuss the main topics addressed with this technique and the degree of development of the contributions.

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