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Found 4 papers, 4 papers with code
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from reinforcement learning.
Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge
Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recent years.
Policy-Based Self-Competition for Planning Problems
AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well.
Inpainting Transformer for Anomaly Detection
Anomaly detection in computer vision is the task of identifying images which deviate from a set of normal images.
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Anomaly Detection
on AeBAD-S
