Q-学习及其在智能机器人局部路径规划中的应用研究

强化学习一词来自于行为心理学，这门学科把行为学习看成反复试验的过程，从而把环境状态映射成相应的动作．在设计智能机器人过程中，如何来实现行为主义的思想、在与环境的交互中学习行为动作？ 文中把机器人在未知环境中为躲避障碍所采取的动作看作一种行为，采用强化学习方法来实现智能机器人避碰行为学习．Ｑ－学习算法是类似于动态规划的一种强化学习方法，文中在介绍了Ｑ－学习的基本算法之后，提出了具有竞争思想和自组织机制的Ｑ－学习神经网络学习算法；然后研究了该算法在智能机器人局部路径规划中的应用，在文中的最后给出了详细的仿真结果

Q-LEARNING AND ITS APPLICATION IN LOCAL PATH PLANNING OF INTELLIGENT ROBOTS

The concept of reinforcement learning comes from behavior psychology that takes behavior learning as trial and error, by which the states of environment are mapped into corresponding actions. There's a question of how the behaviorism can be used to learn the actions in interaction with the environment in designing intelligent robots. In this paper, the actions that a robot takes to avoid obstacles are taken as one class of behaviors and the reinforcement learning is used to realize behavior learning of obstacle avoidance. Q\|learning is one kind of reinforcement learning method that is similar to dynamic programming. After basic ideas of Q\|learning are introduced, a neural network learning algorithm of Q\|learning with concepts of competition and self\|organization is presented. Its application in local path planning of intelligent robots is also introduced. Finally, the detailed simulation results are presented.