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.
Spatial ecology of Norway lobster Nephrops norvegicus in Mediterranean deep-water en- vironments: implications for designing no-take marine reserves
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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.