Facilitating Multiagent Coordination Relying on Graph Information Representation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Multiagent reinforcement learning (MARL) commonly focuses on value functions for coordination.
• Existing MARL methods, under the centralized training with decentralized execution (CTDE) paradigm, often neglect the role of expanded local observations.
• There is a need to improve information sharing and coordination capabilities in MARL systems.

Purpose of the Study:

• To propose a novel method, multigraph-neural-network information representation (MGIR), to enhance coordination in MARL.
• To leverage graph neural networks (GNNs) for richer information extraction between agents.
• To improve the quality of information used by agents during decentralized execution.

Main Methods:

• Modeling multiagent systems (MASs) as graphs to facilitate information extraction.
• Employing multiple GNNs during centralized training to capture diverse perspectives of the MAS.
• Extracting latent variable representations using GNNs for expanded local observations during decentralized execution.

Main Results:

• The proposed MGIR method demonstrated superior coordination performance compared to baseline MARL approaches.
• MGIR effectively extracts rich inter-agent information, leading to higher quality observations.
• Experimental results validate the effectiveness of the GNN-based approach for enhancing MARL coordination.

Conclusions:

• MGIR offers a significant advancement in MARL coordination by effectively utilizing GNNs for information expansion.
• The method can be seamlessly integrated with existing value function decomposition techniques in MARL.
• This approach provides a flexible and powerful tool for improving multiagent system performance.