Reinforcement learning has evolved to tackle domains that are yet to be fully understood, or are too complex for a closed description. In this book the author investigates whether suitable abstraction methods can overcome the discipline's deficiencies.

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.

Book introduces many original ideas Significant contribution to the field of reinforcement learning Contains pointers to future research

Autorentext

Dr. Frommberger is a researcher in the Cognitive Systems Research Group (SFB/TR 8 Spatial Cognition) of Universität Bremen; his special areas of expertise are spatial abstraction techniques, efficient reinforcement learning, cognitive logistics and qualitative representations of space.

Inhalt
Foundations of Reinforcement Learning.- Abstraction and Knowledge Transfer in Reinforcement Learning.- Qualitative State Space Abstraction.- Generalization and Transfer Learning with Qualitative Spatial Abstraction.- RLPR An Aspectualizable State Space Representation.- Empirical Evaluation.- Summary and Outlook.