A JAX-based GPU environment and a transformer-plus-curriculum MARL method train policies that transfer to the Gazebo LRAUV simulator and track up to 5 targets with around 5 m average error.
Multi-AUV cooperative underwa- ter multi-target tracking based on dynamic-switching- enabled multi-agent reinforcement learning
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Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
A JAX-based GPU environment and a transformer-plus-curriculum MARL method train policies that transfer to the Gazebo LRAUV simulator and track up to 5 targets with around 5 m average error.