Zhai Shuo
This review explores the application of reinforcement learning in multi-robot task allocation within the logistics field. With the advancement of technology, intelligent robots are increasingly being utilized across various industries, particularly in complex tasks and diverse environments where multi-robot systems exhibit significant advantages over single-robot systems. Task allocation is a critical component in multi-robot systems. This paper introduces the necessity of multi-robot task allocation, its applications in different domains, and the challenges faced. Traditional algorithms such as linear programming, heuristic search, and swarm intelligence each have their strengths and weaknesses, but they show limitations in dynamic and complex environments. Reinforcement learning, due to its self-learning capability and interaction with the environment, has become a research hotspot. Through continuous exploration and feedback adjustment, reinforcement learning algorithms in multi-robot task allocation gradually approach optimal solutions, demonstrating great potential in this field. Future research in reinforcement learning should focus on lifelong learning algorithms to enhance the efficiency of multi-robot task allocation systems in complex and dynamic logistics and warehousing environments.
Multi-Robot Systems, Task Allocation,Deep Reinforcement Learning