Review of Path Planning Techniques Based on Reinforcement Learning

Abstract

Path planning is one of the key technologies for autonomous navigation of mobile robots.It aims at planning a collision free optimal path from the current position to the destination in real time. This paper introduces the path planning techniques that are based on Reinforcement Learning（RL）and common methods，and categorizes the methods based on RL into two types：the value-based methods and the strategy-based methods.Then the paper compares value-based representation methods（including Timing Difference（TD），Q-Learning，etc.）and the strategy-based representation methods（including Strategy Gradient（SG）and Imitation Learning（IL），etc.），and analyzes the development status of its fusion strategy and Deep Reinforcement Learning（DRL）. On this basis，the paper summarizes the advantages， disadvantages and application scenarios of the RL-based methods. Finally，the future development trends of the path planning techniques based on RL are discussed.