基于糊糙集的改进Q学习算法

An improved Q-learning algorithm based on rough set

针对Q学习算法容易出现错误的时间间隔重叠和高估Q值的情况,进而导致收敛速度慢、学习性能下降的问题,提出了一种改进的Q学习算法,即粗糙集Q学习算法.该算法通过有效处理不完备信息和不确定性知识,使Q值所引起的误差最小化,进而减少Q值的高估,提高学习性能.基于2种算法的机器人自主导航实验结果表明,粗糙集Q学习算法有更高的学习效率和更强的避障能力.

Q-learning algorithm has a fundamental flaw, that is, prone to error intervals overlap, and thus overestimation of the correct Q-value. These are likely to lead to low convergence speed and continuous decline in the performance of Q-learning, an improved Q-learning algorithm was proposed, that was rough sets Q-learning algorithm. The algorithm can he able to minimize the overestimation caused by Q-values and improve performance of learning through effectively deal with incomplete information and uncertain knowledge. Navigation experiments based on these two algorithms were conducted, the results showed that rough sets Q-learning algorithm had higher efficiency of learning and stronger ability of obstacle avoidance than Q-learning algorithm.