Reinforcement-Learning-Based Path Planning: A Reward Function Strategy

Abstract

Path planning is a fundamental task for autonomous mobile robots (AMRs). Classic approaches provide an analytical solution by searching for the trajectory with the shortest distance; however, reinforcement learning (RL) techniques have been proven to be effective in solving these problems with the experiences gained by agents in real time. This study proposes a reward function that motivates an agent to select the shortest path with fewer turns. The solution to the RL technique is obtained via dynamic programming and Deep Q-Learning methods. In addition, a path-tracking control design is proposed based on the Lyapunov candidate function. The results indicate that RL algorithms show superior performance compared to classic A* algorithms. The number of turns is reduced by 50%, resulting in a decrease in the total distance ranging from 3.2% to 36%.