DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat

  • 类型：arxiv
  • 标识：2609.11155
  • 链接：https://arxiv.org/abs/2609.11155
  • 主分类：agent
  • 形态：method
  • TLDR：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, lea
  • 待LLM分类：否
  • 标题中文：DRG-MAPPO：面向协同空战的多智能体强化学习层次动态角色图
  • TLDR中文：多智能体强化学习（MARL）已成为自主系统与空战场景中复杂决策的关键范式。尽管 MARL 在空战中已展现出巨大潜力，实现精细的战术协调仍是一项重大挑战。这一困难主要源于两个局限：（1）缺乏结构化的关系建模，使 agent 难以捕捉战场实体间复杂的、时变的交互；（2）传统扁平架构通常缺乏显式建模战术角色的能力……
  • 来源文件：
  • /inbox/tom/_candidates/2026-09-11-agent-rag-longcontext-candidates.json