Automatic discovery of subgoals based on improved FCM clustering

Authors:
Kun Hu;Xue-Li Yu;Zhi Li
Affiliations:
Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi;Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi;Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Year:
2011

Citing 6
Cited 0

Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching

Machine Learning
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning

ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Options in Reinforcement Learning

Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Dynamic abstraction in reinforcement learning via clustering

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Identifying useful subgoals in reinforcement learning by local graph partitioning

ICML '05 Proceedings of the 22nd international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a learning method which can discover the subgoals on the different state subspaces. It uses the improved fuzzy c-means clustering algorithm to classify the state spaces firstly, and then uses the unique direction value to find the set of subgoals, and finally creates the set of options. The experimental result shows that it can discover the subgoals automatically and quickly. This method can be adapted to the learning tasks under the dynamic audio-visual environment.