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• W3117857532 abstract "Multi-agent reinforcement learning in football simulation can be extended by single-agent reinforcement learning. However, compared with single agents, the learning space of multi-agents will increase dramatically with the increase in the number of agents, so the learning difficulty will also increase. Based on BP neural network as the model structure foundation, this research combines PID controller to control the process of model operation. In order to improve the calculation accuracy to improve the control effect, the prediction output is obtained through the prediction model instead of the actual measured value. In addition, with the football robot as the object, this research studies the multi-agent reinforcement learning problem and its application in the football robot. The content includes single-agent reinforcement learning, multi-agent system reinforcement learning, and ball hunting, role assignment, and action selection in football robot decision strategies based on this. The simulation results show that the method proposed in this paper has certain effects." @default.
• W3117857532 created "2021-01-05" @default.
• W3117857532 creator A5059116190 @default.
• W3117857532 date "2021-04-12" @default.
• W3117857532 modified "2023-09-23" @default.
• W3117857532 title "Simulation of football sport PID controller based on BP neural network" @default.
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• W3117857532 doi "https://doi.org/10.3233/jifs-189570" @default.
• W3117857532 hasPublicationYear "2021" @default.
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