软件与算法改进 Q-learning 算法在路径规划中的应用 摘 要：Q-learning 算法是环境未知条件下的有效强化学习算法，该算法在路径规划中被广泛应用。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，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 算法可以以更快的速度收敛、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。关键词：路径规划；改进 Q-learning 算法；强化学习；栅格法；机器人中图分类号：TP391 文献标志码：AApplication of improved Q-learning algorithm in path planning Abstract: Q-learning algorithm is an effective reinforcement learning algorithm under the condition of unknown environment, which is widely used in path planning. Aiming at the problem of low efficiency and slow learning in discrete state of Q-learning algorithm, 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%. At the same time, the framework of the algorithm has strong generality for solving the same kind of problems.Key words：path planning ; improved Q-learning algorithm; reinforcement learning; grid method; robot0 引言移动机器人可以在人类不可到达或危险未知的地方完成任务，已经成功的运用在很多领域，在移动机器人研究领域中路径规划是一个关键的问题Error:Reference source not found。路径规划问题已经有很多方法可以借鉴，如蚁群算法、人工磁场法、神经网络法等Error: Reference source not found。本文采用改进的 Q-learning算法进行最优路径规划，即指在可以满足预先设定的条件的同时，从起点出发沿最短路径不经过障碍物到达...
