  Machine Learning requires large amounts of labeled data to fit a model. Many
datasets are already publicly available, nevertheless forcing application
possibilities of machine learning to the domains of those public datasets. The
ever-growing penetration of machine learning algorithms in new application
areas requires solutions for the need for data in those new domains. This
thesis works on active learning as one possible solution to reduce the amount
of data that needs to be processed by hand, by processing only those datapoints
that specifically benefit the training of a strong model for the task. A newly
proposed framework for framing the active learning workflow as a reinforcement
learning problem is adapted for image classification and a series of three
experiments is conducted. Each experiment is evaluated and potential issues
with the approach are outlined. Each following experiment then proposes
improvements to the framework and evaluates their impact. After the last
experiment, a final conclusion is drawn, unfortunately rejecting this work's
hypothesis and outlining that the proposed framework at the moment is not
capable of improving active learning for image classification with a trained
reinforcement learning agent.
