Complexity Analysis of Quantum Reinforcement Learning
Quantum reinforcement learning has been systematically presented in a recent paper Dong et al, Quantum reinforce- ment learning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 38, No. 5, pp.1207-1220, 2008, and such results have shown that quantum reinforcement learning is an effective approach for the solutions to some complex problems. The purpose of this paper is to analyze the complexity of quantum reinforcement learning. In particular, storage complexity and exploration complexity are defined and a collection of results are presented to demonstrate such complexities by several simple examples.
Reinforcement learning Quantum reinforcement learning Storage complexity Exploration complexity
CHEN Chunlin DONG Daoyi
Department of Control and System Engineering, Nanjing University, Nanjing 210093, P. R. China School of Engineering and Information Technology, University of New South Wales at the Australian De
国际会议
The 29th Chinese Control Conference(第二十九届中国控制会议)
北京
英文
1-5
2010-07-29（万方平台首次上网日期，不代表论文的发表时间）