Quantum-inspired Reinforcement Learning for Decision-making of Markovian State Transition

摘要：

A novel quantum-inspired reinforcement learning (QiRL) algorithm is proposed for decision-making of Markovian state transition. The QiRL algorithm adopts a probabilistic action selection policy to better balance the tradeoff between exploration and exploitation, which is inspired by the collapse phenomenon in quantum measurement. Several simulated experiments of Markovian state transition demonstrate that QiRL is more robust to learning rates and initial states than traditional reinforcement learning. The QiRL approach provides an effective method for complex decision-making problems.

作者: Daoyi Dong Chunlin Chen

作者单位: School of Engineering and Information Technology, University of New South Wales at the Australian De Department of Control and System Engineering, School of Management and Engineering, Nanjing Universi

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 21-26

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）