This work describes the use some scientific Python tools to solve information gathering problems using Reinforcement Learning. In particular, we focus on the problem of designing an agent able to learn how to gather information in linked datasets. We use four different libraries—RL-Glue, Gensim, NetworkX, and scikit-learn—during different stages of our research. We show that, by using NumPy arrays as the default vector/matrix format, it is possible to integrate these libraries with minimal effort.

Keywords:reinforcement learninglatent semantic analysismachine learning