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Found 3 papers, 1 papers with code
Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
In this paper, we propose a novel representation-based approach to measure the domain gap, where the representation is learned through a contrastive objective by sampling transitions from different domains.
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
Experimental results illustrate the superiority of RO2O in facilitating stable offline-to-online learning and achieving significant improvement with limited online interactions.
AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge
In this study, we introduce \textbf{AttendSeg}, a low-precision, highly compact deep neural network tailored for on-device semantic segmentation.
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