A hierarchical learning architecture with multiple-goal representations based on adaptive dynamic programming
Document Type
Conference Proceeding
Date of Original Version
6-9-2010
Abstract
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. ©2010 IEEE.
Publication Title, e.g., Journal
2010 International Conference on Networking, Sensing and Control, ICNSC 2010
Citation/Publisher Attribution
He, Haibo, and Bo Liu. "A hierarchical learning architecture with multiple-goal representations based on adaptive dynamic programming." 2010 International Conference on Networking, Sensing and Control, ICNSC 2010 (2010): 286-291. doi: 10.1109/ICNSC.2010.5461483.