Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles (arxiv.org)
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 training impractical. Yet, high-fidelity simulation is crucial to test complex policies and close the sim-to-real gap. To address these limitations, we develop a GPU-accelerated environment that achieves up to 30,000x speedup over Gazebo while preserving its dynamics. This enables fast, end-to-end GPU training and seamless transfer to Gazebo for evaluation. We also introduce a Transformer-based architecture (TransfMAPPO) that learns policies invariant to fleet size and number of targets, enabling curriculum learning to train larger fleets on increasingly complex scenarios. After large-scale GPU training, we perform extensive evaluations in Gazebo, showing our method maintains tracking errors below 5m even with multiple fast-moving targets.
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 training impractical. Yet, high-fidelity simulation is crucial to test complex policies and close the sim-to-real gap. To address these limitations, we develop a GPU-accelerated environment that achieves up to 30,000x speedup over Gazebo while preserving its dynamics. This enables fast, end-to-end GPU training and seamless transfer to Gazebo for evaluation. We also introduce a Transformer-based architecture (TransfMAPPO) that learns policies invariant to fleet size and number of targets, enabling curriculum learning to train larger fleets on increasingly complex scenarios. After large-scale GPU training, we perform extensive evaluations in Gazebo, showing our method maintains tracking errors below 5m even with multiple fast-moving targets.
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