• Jun 13, 2017 News!The submission for 2018 7th International Conference on Information and Electronics Engineering (ICIEE 2018) is officially open now !   [Click]
  • Jul 28, 2017 News!IJIEE Vol. 7, No. 4 issue has been published online!   [Click]
  • Jun 28, 2017 News!Papers published in Vol.7, No.1 have all received dois from Crossref.
General Information
Editor-in-chief

 
Faculty of Science, University of Brunei Darussalam, Brunei Darussalam   
" It is a great honor to serve as the editor-in-chief of IJIEE. I'll work together with the editorial team. Hopefully, IJIEE will be recognized among the readers in the related field."
IJIEE 2012 Vol.2(4): 538-542 ISSN: 2010-3719
DOI: 10.7763/IJIEE.2012.V2.156

Hierarchical State Representation and Action Abstractions in Q-Learning for Agent-Based Herding

Tao Mao and Laura E. Ray

Abstract—A primary challenge of agent-based policy learning in complex and uncertain environments is escalating computational complexity with the size of the task space and the number of agents. Nonetheless, there is ample evidence in the natural world that high functioning social mammals learn to solve complex problems with ease. This ability to solve computationally intractable problems stems in part from brain circuits for hierarchical representation of state and action spaces and learned policies arising from these representations. Using such mechanisms for state representation and action abstraction, we constrain state-action choices in reinforcement learning in order to improve learning efficiency and generalization of learned policies within a single-agent herding task. We show that satisficing and generalizable policies emerge, which reduce computational cost, and/or memory resources.

Index Terms—Markov decision process; reinforcement learning; hierarchical state representation; robotic herding.

The authors are with Thayer School of Engineering at Dartmouth College, Hanover, NH 03755, USA (e-mail: tao.mao@dartmouth.edu, laura.e.ray@dartmouth.edu).

[PDF]

Cite: Tao Mao and Laura E. Ray, "Hierarchical State Representation and Action Abstractions in Q-Learning for Agent-Based Herding," International Journal of Information and Electronics Engineering vol. 2, no. 4, pp.538-542, 2012.

Copyright © 2008-2017. International Journal of Information and Electronics Engineering. All rights reserved.
E-mail: ijiee@ejournal.net