Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles

• URL: http://arxiv.org/abs/2505.08222v1
• Date: Tue, 13 May 2025 04:42:30 GMT
• Title: Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
• Authors: Matteo Gallici, Ivan Masmitja, Mario Martín,
• Abstract summary: Multi-Agent Reinforcement Learning is notoriously sample-inefficient.<n>High-fidelity simulators like Gazebo's LRAUV provide 100x faster-than-real-time single-robot simulations.<n>We propose an iterative distillation method that transfers high-fidelity simulations into a simplified, GPU-accelerated environment.
• Score: 0.9217021281095907
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous vehicles (AV) offer a cost-effective solution for scientific missions such as underwater tracking. Recently, reinforcement learning (RL) has emerged as a powerful method for controlling AVs in complex marine environments. However, scaling these techniques to a fleet--essential for multi-target tracking or targets with rapid, unpredictable motion--presents significant computational challenges. Multi-Agent Reinforcement Learning (MARL) is notoriously sample-inefficient, and while high-fidelity simulators like Gazebo's LRAUV provide 100x faster-than-real-time single-robot simulations, they offer no significant speedup for multi-vehicle scenarios, making MARL training impractical. To address these limitations, we propose an iterative distillation method that transfers high-fidelity simulations into a simplified, GPU-accelerated environment while preserving high-level dynamics. This approach achieves up to a 30,000x speedup over Gazebo through parallelization, enabling efficient training via end-to-end GPU acceleration. Additionally, we introduce a novel Transformer-based architecture (TransfMAPPO) that learns multi-agent policies invariant to the number of agents and targets, significantly improving sample efficiency. Following large-scale curriculum learning conducted entirely on GPU, we perform extensive evaluations in Gazebo, demonstrating that our method maintains tracking errors below 5 meters over extended durations, even in the presence of multiple fast-moving targets. This work bridges the gap between large-scale MARL training and high-fidelity deployment, providing a scalable framework for autonomous fleet control in real-world sea missions.

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