Multi-agent proximal policy optimization supported dynamic behavior tree evolution for multi-agent systems

doi:10.1016/j.engappai.2026.113841

2026-01-14

PRE

Koray Özdemir *

Adem Tuncer

DOI：10.1016/j.engappai.2026.113841

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• 结合MAPPO和动态BT演化的决策方法。 • MAPPO学习到的策略使智能体能够根据环境变化调整BT结构。 • 智能体自主更新其BT，实现可扩展的去中心化系统。 • MAPPO优化后的BT提升任务传播效率并减少完成时间。 • MAPPO与BT模块化增强复杂多智能体系统中的决策能力。

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Engineering Applications of Artificial Intelligence

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yalova university

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