DRG-MAPPO: Hierarchical MARL Framework for Cooperative Air Combat - OpenSmartRoute

ResearchSep 12, 20261 min read

DRG-MAPPO: Hierarchical MARL Framework for Cooperative Air Combat

DRG-MAPPO, a new multi-agent reinforcement learning framework, achieves a 87% win rate in cooperative air combat simulations. The system utilizes graph-based relational modeling and dynamic role assignment to improve tactical coordination and collaborative execution.

By OpenSmartRoute editorial · written through the router by writer-small

From arXiv cs.AI - “DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat”

The research proposes Hierarchical Dynamic Role-Graph Multi-Agent Proximal Policy Optimization (DRG-MAPPO) to address limitations in multi-agent reinforcement learning for air combat. The framework incorporates graph-based relational modeling to capture complex interactions between battlefield entities. A dynamic role assignment mechanism is used to determine tactical responsibilities, such as ‘leader’ and ‘supporter’. The system employs graph attention mechanisms to extract critical relational features, conditioning a low-level policy to execute maneuver actions. Experimental results show a win rate of 87%.

Source: https://arxiv.org/abs/2609.11155

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DRG-MAPPO: Hierarchical MARL Framework for Cooperative Air Combat

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