Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams

2025-11-14

AI Summary

Existing methods struggle to balance collaboration efficiency and communication overhead in dynamic, partially observable, communication-constrained, and decentralized multi-agent environments—such as heterogeneous land-air-water unmanned systems. This paper proposes a goal-oriented decentralized multi-agent reinforcement learning framework featuring a novel goal-aware sparse communication mechanism. Each agent autonomously decides whether to communicate and which task-relevant features to share, based solely on its local observations and current sub-goal. This design significantly reduces bandwidth requirements while preserving collaborative performance. Experiments on multi-agent navigation tasks demonstrate that our approach substantially improves task success rates and reduces average time-to-goal. Crucially, performance remains stable as the number of agents scales up, confirming the method’s effectiveness, robustness, and scalability.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multi-Robot Systems
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Connected and autonomous vehicles across land, water, and air must often operate in dynamic, unpredictable environments with limited communication, no centralized control, and partial observability. These real-world constraints pose significant challenges for coordination, particularly when vehicles pursue individual objectives. To address this, we propose a decentralized Multi-Agent Reinforcement Learning (MARL) framework that enables vehicles, acting as agents, to communicate selectively based on local goals and observations. This goal-aware communication strategy allows agents to share only relevant information, enhancing collaboration while respecting visibility limitations. We validate our approach in complex multi-agent navigation tasks featuring obstacles and dynamic agent populations. Results show that our method significantly improves task success rates and reduces time-to-goal compared to non-cooperative baselines. Moreover, task performance remains stable as the number of agents increases, demonstrating scalability. These findings highlight the potential of decentralized, goal-driven MARL to support effective coordination in realistic multi-vehicle systems operating across diverse domains.

Problem

• Decentralized multi-vehicle coordination with limited communication
• Partial observability and dynamic environments challenge agent cooperation
• Individual objectives conflict with team performance in navigation tasks

Innovation

• Decentralized multi-agent reinforcement learning framework
• Goal-aware selective communication strategy
• Scalable performance with dynamic agent populations