  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. In this
paper, we show that 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. We run our algorithm against Microsoft's
LETOR listwise dataset and achieve an NDCG@1 (ranking accuracy in the range
[0,1]) of 0.5075, narrowly beating out the leading supervised learning model,
SVMRank (0.4958).
