Compression and Reuse of knowledge in Q-Learning Based on FCM-type Co-clustering

Abstract

We discuss reuse of the knowledge which is extracted from the Q-table by FCM-type Co-clustering. Advantages and disadvantages of setting the initial values in Q-table based on the extracted cluster data and using the membership in updating of Q-table where several states are updated at same time are reviewed through the path search problem and the single pendulum standing task.