Deep-Q-Rank
Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query. Deep Q-Learning has been shown to be a useful method for training an agent in sequential decision making.
DeepQRank, our deep q-learning to rank agent, demonstrates performance that can be considered state-of-the-art. Though less computationally efficient than a supervised learning approach such as linear regression, our agent has fewer limitations in terms of which format of data it can use for training and evaluation.
Model Details
Model Description
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Model Sources [optional]
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- Paper [https://arxiv.org/abs/2002.07651]: [More Information Needed]
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Uses
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How to Get Started with the Model
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Training Details
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
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