基于DDPG算法的路径规划研究

Research on Path Planning Based on DDPG Algorithm

路径规划是人工智能领域的一个经典问题,在国防军事、道路交通、机器人仿真等诸多领域有着广泛应用,然而现有的路径规划算法大多存在着环境单一、离散的动作空间、需要人工构筑模型的问题。强化学习是一种无须人工提供训练数据自行与环境交互的机器学习方法,深度强化学习的发展更使得其解决现实问题的能力得到进一步提升,本文将深度强化学习的DDPG(Deep Deterministic Policy Gradient)算法应用到路径规划领域,完成了连续空间、复杂环境的路径规划。

Path planning is a classic problem in the field of artificial intelligence,which has been widely used in national defense,military,road traffic,robot simulation and other fields.However,most of the existing path planning algorithms have he problems of single environment,discrete action space,and need to build artificial models.Reinforcement learning is a machine learning meth⁃od that interacts with the environment without providing training data manually,deep reinforcement learning more makes its ability to solve practical problems of the development of further ascension.In this paper,deep reinforcement learning algorithm DDPG(Deep Deterministic Policy Gradient)algorithm is applied in the field of path planning,which completes the task of path planning for continuous space,complex environment.