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.
Lin J, Yang X, Zheng P, et al., 2019, End-To-End Decentralized Multi-Robot Navigation in Unknown Complex Environments Via Deep Reinforcement Learning 2019, IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2493-2500.
Qie H, Shi D, Shen T, et al., 2019, Joint Optimization of Multi-UAV Target Assignment and Path Planning based on Multi-Agent Reinforcement Learning. IEEE access, 7: 146264-146272.
Li B, Wu Y, 2020, Path Planning for UAV Ground Target Tracking Via Deep Reinforcement Learning. IEEE Access, 8: 29064-29074.
Wu D, Wan K, Gao X, et al., 2021, Multiagent Motion Planning Based on Deep Reinforcement Learning in Complex Environments 2021, 6th International Conference on Control and Robotics Engineering (ICCRE). IEEE, 123-128.
Cruz DL, Yu W, 2017, Path Planning of Multi-Agent Systems in Unknown Environment with Neural Kernel Smoothing and Reinforcement Learning. Neurocomputing, 233: 34-42.
Xin J, Zhao H, Liu D, et al., 2017, Application of Deep Reinforcement Learning in Mobile Robot Path Planning 2017, Chinese Automation Congress (CAC). IEEE, 7112-7116.
Yang Y, Juntao L, Lingling P, 2020, Multi-Robot Path Planning based on a Deep Reinforcement Learning DQN Algorithm. CAAI Transactions on Intelligence Technology, 5(3): 177-183.
Liu Z, Chen B, Zhou H, et al., 2020, Mapper: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments 2020 IEEE, RSJ International Conference on Agent Robots and Systems (IROS). IEEE, 11748-11754.
Wang D, Deng H, 2021, Multirobot Coordination with Deep Reinforcement Learning in Complex Environments. Expert Systems with Applications, 180: 115128.
Sartoretti G, Kerr J, Shi Y, et al., 2019, Primal: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning. IEEE Robotics and Automation Letters, 4(3): 2378-2385.
Damani M, Luo Z, Wenzel E, et al., 2021, PRIMAL $ _2 $: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning-Lifelong. IEEE Robotics and Automation Letters, 6(2): 2666-2673.