Contextual multi-task RL for underwater navigation uses just 1.5% of network weights for task differentiation, mostly from context-variable connections to the first hidden layer.
Deep Reinforcement Learning with Double Q- Learning
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A context-dependent multi-task RL policy is trained and evaluated in HoloOcean simulation to solve multiple reef monitoring tasks with claimed improvements in sample efficiency, zero-shot generalization, and robustness to water currents.
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Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation
Contextual multi-task RL for underwater navigation uses just 1.5% of network weights for task differentiation, mostly from context-variable connections to the first hidden layer.
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Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring
A context-dependent multi-task RL policy is trained and evaluated in HoloOcean simulation to solve multiple reef monitoring tasks with claimed improvements in sample efficiency, zero-shot generalization, and robustness to water currents.