A hierarchical learning architecture with multiple-goal representations based on adaptive dynamic programming
Haibo He, Bo Liu · 2010
In this paper we propose a hierarchical learning architecture with multiple-goal representations based on adaptive dynamic programming (ADP). The key idea of this architecture is to integrate a reference network to provide the internal reinforcement representation (secondary reinforcement signal) to interact with the operation of the learning system. Such a reference network serves an important role to build the internal goal representations. Furthermore, motivated by recent research in neurobiological and psychology research, the proposed ADP architecture can be designed in a hierarchical way, in which different levels of internal reinforcement signals can be developed to represent multi-level goals for the intelligent system. Detailed system level architecture, learning and adaptation principle, and simulation results are presented in this work to demonstrate the effectiveness of this work.