Enhancing Collaboration in Heterogeneous Multiagent Systems Through Communication Complementary Graph.

Area of Science:

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning

Background:

• Heterogeneous multiagent systems feature diverse tasks and limited agent observations.
• Parameter sharing is effective in homogeneous systems but costly in heterogeneous ones.
• Existing methods struggle with exploration costs in heterogeneous multiagent collaboration.

Purpose of the Study:

• To propose a novel Communication Complementary Graph Model (CCGM) for enhanced collaboration in heterogeneous multiagent systems.
• To address the challenge of limited observations and high exploration costs in these systems.
• To improve the performance of heterogeneous agent reinforcement learning (HARL) algorithms.

Main Methods:

• Developed a Communication Complementary Graph Model (CCGM) within a HARL framework.
• Utilized advantage function decomposition and sequential updates for policy convergence.
• Introduced a signaling game-inspired communication method for message alignment with observations via a graph module.

Main Results:

• CCGM effectively enhances collaboration in heterogeneous multiagent systems.
• The model demonstrated improved performance in multiagent particle environments (MPE).
• Evaluations in multiagent MuJoCo (MAMuJoCo) robot experiments confirmed CCGM's effectiveness.

Conclusions:

• CCGM offers a novel solution for communication in heterogeneous multiagent systems.
• The proposed model successfully mitigates exploration costs associated with limited observations.
• CCGM significantly improves the capabilities of HARL-based algorithms in complex scenarios.