Journal of System Simulation ›› 2017, Vol. 29 ›› Issue (11): 2782-2787.doi: 10.16182/j.issn1004731x.joss.201711026

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Analysis and Optimization of the Action Chain Mechanism in Agent2D Underlying in RoboCup2D Soccer League

Chen Bing1, Xu Feifan2, Xu Hanyan2, Cheng Zekai3, Liu Cheng1   

  1. 1. School of Arts and Sciences, Information Engineering University, ZhengZhou City, 450001, China;
    2. School of Junior Commanding Officers, Information Engineering University, ZhengZhou City, 450001, China;
    3. School of Computer Science, AnHui University of Technology, Ma Anshan City, 243002, China
  • Received:2016-06-24 Published:2020-06-05

Abstract: In the RoboCup2D soccer league, Agent2D is one of the most widely used underlying team in China. Data transmission noise and the incomplete action chain mechanism make the underlying teams using Agent2D be lack of flexibility. This paper introduces an action correcting parameter and optimizes the operation of the action chain by reinforcement learning mechanism. The performance of the Agent2D underlying team is improved in the game and the adaptability of the team is enhanced. Simulation experiment results show that this method has a certain effect.

Key words: RoboCup2D, Simulation, action chain, action correcting parameter, reinforcement learning

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