A single-task and multi-decision evolutionary game model based on multi-agent reinforcement learning

• 作者: MA Ye CHANG Tianqing FAN Wenhui
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: SYSTEMS ENGINEERING
• 页码范围: 642-657
• 页数: 16页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000055

摘要

In the evolutionary game of the same task for groups,the changes in game rules,personal interests,the crowd size,and external supervision cause uncertain effects on individual decision-making and game results.In the Markov decision framework,a single-task multi-decision evolutionary game mo-del based on multi-agent reinforcement learning is proposed to explore the evolutionary rules in the process of a game.The model can improve the result of a evolutionary game and facili-tate the completion of the task.First,based on the multi-agent theory,to solve the existing problems in the original model,a negative feedback tax penalty mechanism is proposed to guide the strategy selection of individuals in the group.In addition,in order to evaluate the evolutionary game results of the group in the model,a calculation method of the group intelligence level is defined.Secondly,the Q-learning algorithm is used to improve the guiding effect of the negative feedback tax penalty mecha-nism.In the model,the selection strategy of the Q-learning al-gorithm is improved and a bounded rationality evolutionary game strategy is proposed based on the rule of evolutionary games and the consideration of the bounded rationality of indi-viduals.Finally,simulation results show that the proposed mo-del can effectively guide individuals to choose cooperation strategies which are beneficial to task completion and stability under different negative feedback factor values and different group sizes,so as to improve the group intelligence level.