This paper is concerned with the problem of a novel Q-learning algorithm for solving optimal cost function.
该文利用求解最优费用函数的方法给出了一种新的Q学习算法。
In order to enhance the study speed and the convergence rate of Q-learning algorithm, an algorithm that based on the experience knowledge about environment is proposed.
为了提高智能体系统中的典型的强化学习——Q -学习的学习速度和收敛速度，使学习过程充分利用环境信息，本文提出了一种基于经验知识的Q -学习算法。
The improved Q learning algorithm was suggested because of the traditional algorithm has limitations of slow and partial constringency.
传统的Q学习存在收敛速度慢和容易导致局部收敛的矛盾，为此提出一种改进的Q学习算法。
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