כתבה
arXiv cs.AI ·
DRG-MAPPO: למידת תגמול רב-סוכנים
DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat
DRG-MAPPO הוא כלי למידת תגמול רב-סוכנים לקרבות אוויר. הוא משתמש בגרפים ותשומת לב לדגמים סבוכים. ניסויים הראו 87% ניצחון.
תקציר מקורי באנגליתarXiv:2609.11155v1 Announce Type: new Abstract: Multi-Agent Reinforcement Learning (MARL) has emerged as a pivotal paradigm for complex decision-making in autonomous systems and air combat. While MARL has demonstrated significant potential in air combat, achieving sophisticated tactical coordination remains a non-trivial challenge. This difficulty is largely attributed to two primary limitations: (1) the absence of structured relational modeling hinders agents from capturing complex, time-varying interactions among battlefield entities; and (2) conventional flat architectures often lack the capability to explicitly model tactical roles, leading to ambiguous task allocation in highly dynamic environments. To address these challenges, we propose Hierarchical Dynamic Role-Graph Multi-Agent Pr
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