Heterogeneous Demand-Aware Multi-Agent Communication Based on Role Representation

Area of Science:

• Artificial Intelligence
• Multi-Agent Reinforcement Learning (MARL)
• Communication Protocols

Background:

• Efficient communication is crucial for multi-agent reinforcement learning (MARL) systems to overcome partial observability and enhance decision-making.
• Current MARL research often focuses on teammate modeling or intention inference, but overlooks the policy-generating guidance of communication.
• Heterogeneous agents with varying intentions in collaborative scenarios pose challenges for accurate inference and effective communication.

Purpose of the Study:

• To propose a novel communication framework, Heterogeneous Demand-Aware Multi-Agent Communication (HDMAC), to improve implicit collaboration among agents.
• To address the limitations of existing methods by incorporating heterogeneous demands and enhancing the policy-generating role of communication.
• To improve training efficiency through knowledge distillation and an approximation of the ideal policy.

Main Methods:

• HDMAC utilizes a role representation protocol for dynamic generation of agent roles.
• It extracts teammate demands from minimal messages and generates customized messages using a cross-attention mechanism correlating demands with local observations.
• A knowledge distillation approach is employed to approximate the ideal policy for enhanced training efficiency.

Main Results:

• HDMAC significantly outperforms existing baseline algorithms in both homogeneous and heterogeneous collaborative MARL environments.
• The proposed role representation protocol effectively improves implicit collaboration among agents.
• The method demonstrates enhanced training efficiency and communication effectiveness.

Conclusions:

• HDMAC offers a significant advancement in MARL by enabling more effective communication and collaboration, particularly in heterogeneous agent settings.
• The framework successfully addresses the limitations of previous approaches by integrating demand awareness and improving the policy-generating function of messages.
• HDMAC provides a robust solution for enhancing performance in complex collaborative multi-agent systems.