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Found 2 papers, 1 papers with code
Reinforced In-Context Black-Box Optimization
In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
Model Generation with Provable Coverability for Offline Reinforcement Learning
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could adapt to different dynamics enumerated at the training stage.
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