Quantum reinforcement learning

Authors:
Daoyi Dong;Chunlin Chen;Zonghai Chen
Affiliations:
Department of Automation, University of Science and Technology of China, Hefei, Anhui, People's Republic of China;Department of Automation, University of Science and Technology of China, Hefei, Anhui, People's Republic of China;Department of Automation, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 4
Cited 4

Technical Note: \cal Q-Learning

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences

Machine Learning

A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory

International Journal of Intelligent Systems Technologies and Applications
An autonomous mobile robot based on quantum algorithm

CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Grey reinforcement learning for incomplete information processing

TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Quantum speed-up for unsupervised learning

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introduced. The state/action value is represented with quantum state and the probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. This approach makes a good tradeoff between exploration and exploitation using probability and can speed up learning. The results of simulated experiment verified its effectiveness and superiority.