分层强化学习让无人机在空战中协同作战更高效。
Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning
- 分两层决策：底层控飞行，顶层定战术。
- 复杂空战中学习更快，胜率更高。
- 适合研究多机协同与智能决策的学者。
由于情境感知不全和非线性飞行动力学，在真实模拟空战中达成任务目标极具挑战。本文提出一个新颖的三维多智能体空战环境和分层多智能体强化学习框架以应对这些难题。该方法结合异构智能体动力学、课程学习、联赛对战及新适配的训练算法。决策过程分为两个抽象层级：低层策略学习精确操控动作，高层策略基于任务目标发出战术指令。实验结果表明，该分层方法在复杂缠斗场景中提升了学习效率与战斗表现。
原文摘要 · Abstract (English)
Achieving mission objectives in a realistic simulation of aerial combat is highly challenging due to imperfect situational awareness and nonlinear flight dynamics. In this work, we introduce a novel 3D multi-agent air combat environment and a Hierarchical Multi-Agent Reinforcement Learning framework to tackle these challenges. Our approach combines heterogeneous agent dynamics, curriculum learning, league-play, and a newly adapted training algorithm. To this end, the decision-making process is organized into two abstraction levels: low-level policies learn precise control maneuvers, while high-level policies issue tactical commands based on mission objectives. Empirical results show that our hierarchical approach improves both learning efficiency and combat performance in complex dogfight scenarios.
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