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Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning
Applying diffusion models in reinforcement learning for long-term planning has gained much attention recently.
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
In offline reinforcement learning (RL), the performance of the learned policy highly depends on the quality of offline datasets.
Learning the Network of Graphs for Graph Neural Networks
Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations.
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