未知环境下基于有先验知识的滚动Q学习机器人路径规划

提出一种未知环境下基于有先验知识的滚动Q学习机器人路径规划算法.该算法在对Q值初始化时加入对环境的先验知识作为搜索启发信息,以避免学习初期的盲目性,可以提高收敛速度.同时,以滚动学习的方法解决大规模环境下机器人视野域范围有限以及因Q学习的状态空间增大而产生的维数灾难等问题.仿真实验结果表明,应用该算法,机器人可在复杂的未知环境中快速地规划出一条从起点到终点的优化避障路径,效果令人满意.

Path planning of robot for unknown environment based on prior knowledge rolling Q-learning

A path planning of rolling Q-learning algorithm based on the prior knowledge in the unknown environment is
proposed. The prior knowledge about the environment is added as heuristic information of Q learning to initialize the value, so as to avoid the blindness of early-stage learning and improve rate of convergence. Besides, the method of rolling learning is used for solving the problems of limited visual domain of the robot as well as dimensionality disaster caused by the increase in state space of  Q-learning in a large scale environment. The simulation results show that, the robot can not only avoid collision safely, but also find out an optimal path by using the algorithm in the unknown environment, and the results obtained are satisfactory.