---
title: "Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning"
authors:
  - "Ah-Hwee Tan"
doi: ""
url: "https://ijcai.org/proceedings/2007/173"
pdf: "https://ijcai.org/proceedings/2007/0173.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1071-1075"
track: ""
keywords:
---

# Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning

Ah-Hwee Tan

*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.