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

• URL: http://arxiv.org/abs/2511.11992v1
• Date: Sat, 15 Nov 2025 02:11:31 GMT
• Title: Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
• Authors: Hung Du, Hy Nguyen, Srikanth Thudumu, Rajesh Vasa, Kon Mouzakis,
• Abstract summary: 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.<n>This goal-aware communication strategy allows agents to share only relevant information, enhancing collaboration while respecting visibility limitations.<n>Results show that our method significantly improves task success rates and reduces time-to-goal compared to non-cooperative baselines.
• Score: 0.6676697660506798
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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