Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

Abstract

With the rapid advancement of deep reinforcement learning (DRL) in multi-agent systems, a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning (MADRL) are surfacing. Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently, and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence. In this paper, we sort out the training methods for multi-robot path planning, as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods; finally, we suggest possible research directions for researchers.

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

“…For this application, model-free DRL algorithms are predominant, probably due to the complexity of modelling a dynamic environment [129]. Based on the analysed research articles, DQN, together with its variants, is the most used one [130][131][132].…”