GLiBRL uses GLMs with learnable basis functions for exact Bayesian inference in deep BRL, derives a closed-form link between L2 task distances and kernel task similarity, and reports up to 1.8x gains over prior meta-RL on MuJoCo and MetaWorld.
Single episode policy transfer in reinforcement learning
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AIDA augments scarce target data for sim-to-real visual RL by adaptively truncating unreliable imagined rollouts via a distribution-shift-aware discriminator and applying self-consistency loss on reliable state reconstructions.
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Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data
AIDA augments scarce target data for sim-to-real visual RL by adaptively truncating unreliable imagined rollouts via a distribution-shift-aware discriminator and applying self-consistency loss on reliable state reconstructions.