改进Q-Learning 算法在路径规划中的应用

针对Q-Learning 算法在离散状态下存在运行效率低、学习速度慢等问题，提出一种改进的Q-Learning 算法。改进后的算法在原有算法基础上增加了一层学习过程，对环境进行了深度学习。在栅格环境下进行仿真实验，并成功地应用在多障碍物环境下移动机器人路径规划，结果证明了算法的可行性。改进Q-Learning 算法以更快的速度收敛，学习次数明显减少，效率最大可提高20%。同时，该算法框架对解决同类问题具有较强的通用性。

Application of Improved Q-Learning Algorithm in Path Planning

Aiming at the problem of low efficiency and slow learning in discrete state of Q-Learning algorithm．The improved algorithm adds a learning process on the basis of the original algorithm，and makes deep learning of the environment．An improved Q-Learning algorithm is proposed to simulate in grid environment． It has been successfully applied to the path planning of a mobile robot in a multi barrier environment，and the results prove the feasibility of the algorithm． The improved Q-Learning algorithm can converge faster，reduce the number of learning，and increase the efficiency by 20%． The framework of the algorithm has strong generality for solving the same kind of problems．