Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2007
Abstract
TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this deficiency, this paper presents a direct code access procedure whereby TD-FALCON conducts instantaneous searches for cognitive nodes that match with the current states and at the same time provide maximal reward values. Our comparative experiments show that TD-FALCON with direct code access produces comparable performance with the original TD-FALCON while improving significantly in computation efficiency and network complexity.
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI'07), Hyderabad, India, 2007 January 6-12
First Page
1
Last Page
6
Identifier
10.5555/1625275.1625449
Publisher
AAAI
City or Country
Hyderabad, India
Citation
TAN, Ah-hwee.
Direct code access in self-organizing neural networks for reinforcement learning. (2007). Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI'07), Hyderabad, India, 2007 January 6-12. 1-6.
Available at: https://ink.library.smu.edu.sg/sis_research/6764
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.