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Found 2 papers, 1 papers with code
Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research
Since the introduction of DQN, a vast majority of reinforcement learning research has focused on reinforcement learning with deep neural networks as function approximators.
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data
This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data.
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