How can RL optimize multi-robot task allocation?
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays
February 25, 2025
https://arxiv.org/pdf/2502.16079This paper tackles the problem of efficiently assigning tasks to multiple robots in dynamic environments like warehouses, aiming to minimize travel time and task completion delays. It introduces MRTAgent, a two-agent reinforcement learning system where one agent selects tasks and the other assigns robots. Key aspects relevant to LLM-based multi-agent systems include the hierarchical, cooperative nature of the agents, the use of reinforcement learning for coordination and adaptation to real-time changes, and the potential for this approach to be generalized to other multi-agent domains beyond robotics.