How can MARL optimize resource allocation?
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
This paper surveys Multi-Agent Reinforcement Learning (MARL) for optimizing resource allocation. It examines how MARL addresses challenges in dynamic, decentralized environments where multiple agents need to share limited resources, like in cloud computing, energy grids, and telecommunications.
Key points for LLM-based multi-agent systems: MARL handles complex real-world problems by adapting to changing conditions, operating with decentralized control, and scaling to large systems. The survey highlights centralized training with decentralized execution (CTDE) as a particularly relevant paradigm for LLM agents, allowing for coordinated training while maintaining decentralized adaptability. It discusses Dec-POMDPs as a framework for modeling partially observable multi-agent environments, where agents have limited information – a common scenario for LLM-based agents. The paper also highlights the need for future research in scalability, adaptability, and agent communication for even more complex scenarios, particularly relevant for evolving LLM-driven multi-agent applications. It lists real-world simulators and benchmarks like CityFlow (traffic management) and bsk_rl (satellite mission control), potentially useful for developing and evaluating LLM-based multi-agent systems.