【作者】 孟祥萍； 苑全德； 皮玉珍；

【Author】 Xiangping Meng Quande Yuan Yuzhen Pi (Department of Electrical and Information Engineering, Changchun Institute of Technology, Changchun, 130012) (Department of Information Engineering, Northeast Dianli University, Jilin, 132012)

【机构】 长春工程学院电气与信息工程学院； 东北电力大学信息工程学院；

【摘要】 针对多Agent系统强化学习中存在的状态空间极大时,传统的强化学习算法学习速度缓慢等问题,本文试图将量子计算理论与多Agent系统强化学习理论结合,以加快状态空间的搜索和更新速度,缩短计算时间,提高学习效率。仿真效果比较理想。更多还原

【Abstract】 In this paper, we present a novel MAS Reinforcement Learning Algorithm based on Quantum Theory. When the state space large enough, the classical reinforcement learning algorithm is very slow. So we try to use quantum theory in MAS reinforcement learning to speed up learning. The simulate result show that it can exhibit substantial improvements.更多还原