Book — 1 online resource (xvii, 174 pages) : illustrations Digital: text file; PDF.
Foundations of Reinforcement Learning
Abstraction and Knowledge Transfer in Reinforcement Learning
Qualitative State Space Abstraction
Generalization and Transfer Learning with Qualitative Spatial Abstraction
An Aspectualizable State Space Representation
Summary and Outlook.
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.