Reinforcement learning using Voronoi space division

Authors:
Kathy Thi Aung;Takayasu Fuchida
Affiliations:
Graduate School of Science and Engineering, Department of Information and Computer Science, Faculty of Engineering, Kagoshima University, Kagoshima, Japan 890-0065;Graduate School of Science and Engineering, Department of Information and Computer Science, Faculty of Engineering, Kagoshima University, Kagoshima, Japan 890-0065
Venue:
Artificial Life and Robotics
Year:
2010

Citing 3
Cited 1

Experiments with reinforcement learning in problems with continuous state and action spaces

Adaptive Behavior
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Q-Learning in Continuous State and Action Spaces

AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence

A comparison of learning performance in two-dimensional Q-learning by the difference of Q-values alignment

Artificial Life and Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.