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Bachelor Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 1.0, University of Duisburg-Essen, language: English, abstract: Reinforcement learning is a learning problem in which an actor has to behave optimally in its environment. Deep learning methods, on the other hand, are a subclass of representation learning, which in turn focuses on extracting the necessary features for the task (e.g. classification or detection). As such, they serve as powerful function approximators. The combination of those two paradigm results in deep reinforcement learning. This thesis gives an overview of the recent advancement in the field. The results are divided into two broad research directions: value-based and policy-based approaches. This research shows several algorithms from those directions and how they perform. Finally, multiple open research questions are addressed and new research directions are proposed.