Reinforcement learning using Voronoi space division

Reinforcement learning is considered an important tool for robotic learning in unknown/uncertain environments. In this article, we suggest that Voronoi space division creates a new Voronoi region which permits an arbitrary point in the plane, say a Voronoi Q-value element (VQE), and constructs a new method for space division using a Voronoi diagram in order to realize multidimensional reinforcement learning. This article shows some results for four-dimensional spaces, and the essential characteristics of VQEs in a continuous state and action are also described. The advantages of learning with a variety of VQEs are enhanced learning speed and reliability for this task.