Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2016
Abstract
We propose a new, core task abstraction (CTA) approach to learning the relevant transition functions in model-based hierarchical reinforcement learning. CTA exploits contextual independences of the state variables conditional on the task-specific actions; its promising performance is demonstrated through a set of benchmark problems.
Keywords
hierarchical reinforcement learning, core task abstraction
Discipline
Computer Sciences | Numerical Analysis and Scientific Computing | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems: May 9-13, 2016, Singapore
First Page
1411
Last Page
1412
ISBN
9781450342391
Publisher
IFAAMAS
City or Country
Ann Arbor, MI
Citation
LI, Zhuoru; NARAYAN, Akshay; and Tze-Yun LEONG.
A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]. (2016). AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems: May 9-13, 2016, Singapore. 1411-1412.
Available at: https://ink.library.smu.edu.sg/sis_research/3431
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.ifaamas.org/Proceedings/aamas2016/pdfs/p1411.pdf