基于未知环境状态新定义及知识启发的机器人导航Q学习算法

由于强大的自主学习能力, 强化学习方法逐渐成为机器人导航问题的研究热点, 但是复杂的未知环境对算法的运行效率和收敛速度提出了考验。提出一种新的机器人导航Q学习算法, 首先用三个离散的变量来定义环境状态空间, 然后分别设计了两部分奖赏函数, 结合对导航达到目标有利的知识来启发引导机器人的学习过程。实验在Simbad仿真平台上进行, 结果表明本文提出的算法很好地完成了机器人在未知环境中的导航任务, 收敛性能也有其优越性。

A Q-Learning Algorithm for Robot Navigation Based on a New Definition of an Unknown Environment States and Knowledge Heuristic

Due to powerful self-learning ability, reinforcement learning has become a research hot spot about robot navigation problems, but the operating efficiency and convergence speed of the algorithm are tried by the the complex unknown environment. A new Q-learning algorithm for robot navigation was proposed in this paper. First, three discrete variables were used to define the space states of the environment, and then two parts of the reward functions were designed, combining the beneficial knowledge for reaching the target to inspire and guide the robot's learning process. The experiment was executed on Simbad simulation platform. The results show that the proposed algorithm is well done in an unknown environment robot navigation task, and has a better convergence speed.