Qualitative Spatial Abstraction in Reinforcement Learning Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However reinforcement learning does not scale well to large and continuous problems. Furthermore acquired knowledge specific to the learned task and transfer of knowledge to new tasks is crucial. In this book the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction in particular qualitative abstraction a form of representing knowledge that has been thoroughly investigated and successfully applied in spatial cognition research. With his approach he exploits spatial structures and structural similarity to support the learning process by abstracting from less important features and stressing the essential ones. The author demonstrates his learning approach and the transferability of knowledge by having his system learn in a virtual robot simulation system and consequently transfer the acquired knowledge to a physical robot. The approach is influenced by findings from cognitive science. The book is suitable for researchers working in artificial intelligence in particular knowledge representation learning spatial cognition and robotics.