但一直以来学习速度慢和学习效率低的问题严重阻碍了强化学习应用于具有大规模状态空间的复杂问题。
But the slow learning process and low learning performance of RL becomes a formidable obstacle to prevent RL from problems with large state space.
经验证(PSO)优化算法可以有效地克服BP神经网络存在的学习效率低，收敛速度慢以及容易陷入局部极小点等固有缺点。
It is confirmed that PSO could overcome intrinsic shortcomings of BP neural network, including low learning efficiency, slow convergence rate, being easy to fall into local minima, etc.
但是实际应用环境中的数据属性维数非常多，属性概念层次也非常复杂，基于集合论的传统学习方法的效率变得越来越低。
But with the number of attribute increasing and the more and more complicated concept levels, the traditional method based on the set theory becomes lower and lower efficient.
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