Can multi-agent RL optimize dynamic task assignments?
Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems
This paper introduces REDA (RL-Enabled Distributed Assignment), a multi-agent reinforcement learning (MARL) algorithm for optimizing sequential assignment problems, where agents must be repeatedly assigned to tasks in a dynamic environment. It uses a novel approach combining independent Q-learning with a distributed optimal assignment mechanism, enabling scalable solutions to complex, state-dependent assignments. REDA learns the long-term value of assigning each agent to each task and then uses those learned values to generate the jointly optimal assignments at each timestep.
Key points for LLM-based multi-agent systems: REDA provides a structure for coordinating multiple agents in a shared task using individually learned Q-functions to inform a centralized decision-making process. This approach could be valuable for LLM agents where each agent develops specialized knowledge, which is then used for cooperative decision-making in complex, evolving scenarios, potentially surpassing the performance of independent or purely cooperative agents. REDA also suggests methods for handling constraints (like only one agent per task), which is a common challenge in multi-agent LLM systems. Finally, the concept of bootstrapping from a simple greedy policy could help accelerate training in LLM agent systems.