Dynamic Team Combination and Role Balancing Research of Basketball Game Based on Multi-Intelligent Body Co-Optimisation and Deep Reinforcement Learning

DOI: https://doi.org/10.62517/jbdc.202401318

Author(s)

Yuxuan Tong*

Affiliation(s)

Swinburn College, Shandong University of Science and Technology, Shandong, China *Corresponding author

Abstract

This review predictive paper provides an in-depth discussion on the application of Multi-Agent Systems (MAS) and Deep Reinforcement Learning (DRL) techniques in dynamic team combination and character balancing for basketball games. The paper first outlines the basic principles of MAS and DRL techniques and analyses their importance in AI design for basketball games. Then, the paper discusses in detail how these techniques can facilitate the synergistic optimisation between team members and the balancing between roles in basketball games. In addition, the strategy generation and adaptation of these techniques in dynamic environments is explored, as well as how they can provide players with a personalised gameplay experience. Finally, the paper discusses the limitations of the current technologies and offers predictions for future research directions.

Keywords

Multi-Intelligent System (Mas); Deep Reinforcement Learning (Drl); Game AI, Strategy Optimisation; Personalised Experience

References

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