Bi-level graph attention paradigm with differential strategy integration for heterogeneous multi-agent reinforcement...

Area of Science:

• Artificial Intelligence
• Multi-Agent Systems
• Reinforcement Learning

Background:

• Heterogeneous Multi-Agent Systems (MAS) face challenges in communication and coordination due to increasing agent numbers and diverse capabilities.
• Existing frameworks struggle to manage complex interactions and decision-making in large-scale, heterogeneous environments.

Purpose of the Study:

• To propose a novel policy-based group learning framework, Bi-level Graph Attention Paradigm (Bi-GAP), for heterogeneous MAS.
• To address challenges in communication, coordination, and decision-making in both discrete and continuous domains.
• To enhance the robustness and adaptability of MAS under interference.

Main Methods:

• Developed a bi-level graph attention architecture to model agent interactions hierarchically within and across groups.
• Integrated differential strategy with multi-perspective guidance, balancing global coordination with local reasoning.
• Implemented a policy-based group learning framework for heterogeneous agents.

Main Results:

• Bi-GAP demonstrated superior performance compared to state-of-the-art Multi-Agent Reinforcement Learning (MARL) baselines.
• The framework showed consistent outperformance across both discrete (StarCraft II micromanagement) and continuous (Multi-Agent Particle Environment Predator-Prey) settings.
• Bi-GAP achieved flexible and selective communication, reducing message overhead and improving MAS robustness.

Conclusions:

• Bi-GAP offers an effective solution for coordination and decision-making in complex heterogeneous MAS.
• The proposed framework advances the field of MARL by providing a robust and adaptable approach.
• Bi-GAP's hierarchical structure and integrated strategies are key to its success in diverse environments.