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.
Recommended Citation
Bing, Chen; Xu, Feifan; Xu, Hanyan; Cheng, Zekai; and Cheng, Liu
(2020)
"Analysis and Optimization of the Action Chain Mechanism in Agent2D Underlying in RoboCup2D Soccer League,"
Journal of System Simulation: Vol. 29:
Iss.
11, Article 26.
DOI: 10.16182/j.issn1004731x.joss.201711026
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol29/iss11/26
First Page
2782
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201711026
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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