- Full Description
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.
- Table of Contents
Table of Contents
- 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.
If you think that you've found an error in this book, please let us know by emailing to email@example.com . You will find any confirmed erratum below, so you can check if your concern has already been addressed. No errata are currently published