How can I efficiently train cooperative agents using inter-agent coupling?
Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR
This paper proposes a method to make reinforcement learning more efficient for teams of AI agents ("multi-agent systems") working together on a shared task. It focuses on scenarios where agents can affect each other's performance through their actions, the information they have access to, or shared goals. The key idea is to decompose the overall problem into smaller, individual problems for each agent based on how they are coupled, allowing them to learn more efficiently. This decomposition reduces the overall complexity and the amount of data needed for training, especially when compared to a centralized approach where all agents learn from a single, shared model. This concept is relevant to LLM-based multi-agent systems as it offers a path toward scaling multi-agent systems involving LLMs, potentially improving training efficiency and reducing computational demands when dealing with complex interactions and vast amounts of data.