Automatic Adaptive Space Segmentation for Reinforcement Learning
Yuki Komori, Akira Notsu, Katsuhiro Honda, Hidetomo Ichihashi · International Journal of Fuzzy Logic and Intelligent Systems · 2012
We tested a single pendulum simulation and observed the influence of several situation space segmentation types in reinforcement learning processes in order to propose a new adaptive automation for situation space segmentation. Its segmentation is performed by the Contraction Algorithm and the Cell Division Approach. Also, its automation is performed by "entropy," which is defined on action values’ distributions. Simulation results were shown to demonstrate the influence and adaptability of the proposed method.