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.
Reverse-Engineering Memory in DreamerV3: From Sparse Representations to Functional Circuits
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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.