Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning

2025-02-06

AI Summary

Most existing multi-agent reinforcement learning (MARL) methods rely on pairwise interaction modeling, failing to capture higher-order and indirect coordination relationships. To address this, we propose the Deep Meta-Coordination Graph (DMCG), the first framework unifying higher-order interactions and multi-hop indirect paths within a coordination graph architecture. DMCG dynamically generates meta-graph structures tailored to arbitrary interaction types and path lengths. Our approach jointly optimizes graph topology and policy networks in an end-to-end manner by integrating meta-graph structure learning with graph convolutional networks (GCNs). Evaluated on multiple cooperative MARL benchmarks, DMCG achieves significantly improved sample efficiency and surpasses current state-of-the-art methods. Crucially, in several complex scenarios where prior approaches fail to converge, DMCG attains stable task success—demonstrating both the effectiveness and necessity of explicit higher-order coordination modeling.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Graph-based Machine Learning
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordination graph formulations encode local interactions and accordingly factorize the joint value function of all agents to improve efficiency in MARL. However, existing approaches rely solely on pairwise relations between agents, which potentially oversimplifies complex multi-agent interactions. DMCG goes beyond these simple direct interactions by also capturing useful higher-order and indirect relationships among agents. It generates novel graph structures accommodating multiple types of interactions and arbitrary lengths of multi-hop connections in coordination graphs to model such interactions. It then employs a graph convolutional network module to learn powerful representations in an end-to-end manner. We demonstrate its effectiveness in multiple coordination problems in MARL where other state-of-the-art methods can suffer from sample inefficiency or fail entirely. All codes can be found here: https://github.com/Nikunj-Gupta/dmcg-marl.

Problem

• Enhances multi-agent reinforcement learning efficiency.
• Models complex higher-order agent interactions.
• Improves sample efficiency in MARL tasks.

Innovation

• Captures higher-order agent relationships
• Generates novel multi-hop graph structures
• Uses graph convolutional networks end-to-end