Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
arXiv:2505.08222v3 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking. Reinforcement learning (RL) has emerged as a powerful method for controlling AVs, but scaling to fleets (essential for multi-target tracking or rapidly moving targets) is challenging. Multi-Agent RL (MARL) is notoriously sample-inefficient, and while high-fidelity simulators like Gazebo's LRAUV provide up to 100x faster-than-real-time single-robot simulations, they offer little speedup in multi-vehicle scenarios, making MARL tr
The development of more sample-efficient Multi-Agent Reinforcement Learning (MARL) techniques is critical for deploying large fleets of autonomous vehicles, especially as high-fidelity single-robot simulators reach their limits in multi-agent scenarios.
This research addresses a key bottleneck in scaling autonomous systems for complex, real-world applications like underwater tracking, which has significant implications for defense, scientific exploration, and resource management.
The ability to efficiently scale MARL for autonomous vehicles allows for more robust and cost-effective deployment of robotic fleets, shifting the paradigm from single-unit control to coordinated multi-agent operations.
- · Defense contractors
- · Oceanographic research institutions
- · AI/ML companies specializing in MARL
- · Manufacturers of autonomous underwater vehicles
- · Traditional manned survey vessels
- · Organizations reliant on inefficient single-robot deployments
Increased efficiency and capability in underwater surveillance and data collection through autonomous vehicle fleets.
Accelerated development of other multi-robot systems for air, land, and space applications due to advances in MARL scalability.
Enhanced national security and strategic advantage for countries deploying advanced autonomous underwater swarms, potentially impacting geopolitical dynamics.
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Read at arXiv cs.AI