Autonomous Learning and Navigation of Mobile Robots Based on Deep Reinforcement Learning

Abstract

Aiming at the problems of convergence difficulties faced by deep reinforcement learning algorithms in dynamic pedestrian environments, and insufficient reward and feedback mechanisms, a data-driven and model-driven navigation algorithm which named GRRL has been proposed. In order to enrich and perfect the reward feedback mechanism, we designed a dynamic reward function. The reward function fully considers the relationship between the robot and the pedestrian and the target position. It mainly includes three parts. The experimental results show that the autonomous learning efficiency and the average navigation success rate of the mobile robot driven by the GRRL algorithm are improved, the average navigation time is shorter. The dynamic reward function we designed has a certain improvement effect on robot navigation.