Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment

Abstract

The path planning for unmanned mobile robots has always been a crucial issue, especially in unknown environments. Reinforcement learning widely used in path planning due to its ability to learn from unknown environments. But, in unknown environments, deep reinforcement learning algorithms have problems such as long training time and instability. In this article, improvements have been made to the deep deterministic policy gradient algorithm (DDPG) to address the aforementioned issues. Firstly, the experience pool is divided into different experience pools based on the difference between adjacent states; Secondly, experience is collected from various experience pools in different proportions for training, enabling the robot to achieve good obstacle avoidance ability; Finally, by designing a guided reward function, the convergence speed of the algorithm has been improved, and the robot can find the target point faster. The algorithm has been tested in practice and simulation, and the results show that it can enable robots to complete path planning tasks in complex unknown environments.