Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1071-1075.
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.
@inproceedings{ijcai2007p173,
title = {Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning},
author = {Tan, Ah-Hwee},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1071--1075},
year = {2007},
url = {https://ijcai.org/proceedings/2007/173},
}
TY - CPAPER TI - Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning AU - Tan, Ah-Hwee T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1071 EP - 1075 UR - https://ijcai.org/proceedings/2007/173 ER -Copied
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