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Journal of System Simulation

Abstract

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.

First Page

2782

Last Page

2787

CLC

TP391.9

Recommended Citation

Chen Bing, Xu Feifan, Xu Hanyan, Cheng Zekai, Liu Cheng. Analysis and Optimization of the Action Chain Mechanism in Agent2D Underlying in RoboCup2D Soccer League[J]. Journal of System Simulation, 2017, 29(11): 2782-2787.

DOI

10.16182/j.issn1004731x.joss.201711026

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