Abstraction and knowledge transfer in reinforcement learning

Abstract

This chapter presents the state of the art in research on reinforcement learning with a focus on abstraction and transfer learning. Especially, the open questions of performance in large, continuous state spaces and knowledge transfer are worked out as central challenges of reinforcement learning with regard to applications in Sect. 3.1. In the following, three approaches to tackle these problems are investigated: value function approximation (Sect. 3.2), temporal abstraction (Sect. 3.3), and spatial abstraction (Sect. 3.4).