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Found 3 papers, 2 papers with code
Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Models
It iteratively leverages a guided diffusion world model to directly evaluate the offline target policy with actions drawn from it, and then performs an importance-sampled world model update to adaptively align the world model with the updated policy.
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking
In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution.
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