Can multi-agent RL improve power grid control?
CENTRALLY COORDINATED MULTI-AGENT REINFORCEMENT LEARNING FOR POWER GRID TOPOLOGY CONTROL
This research explores how multi-agent reinforcement learning (MARL) can improve the control of power grids, particularly concerning topology changes like rerouting electricity around проблемные areas. They propose a centrally coordinated multi-agent system (CCMA) where regional agents suggest actions, and a central agent makes the final decision. This approach simplifies the complex problem by breaking it down into smaller parts.
Key points for LLM-based multi-agent systems: The CCMA architecture provides a framework for coordinating multiple LLM agents. The use of a central coordinator allows for the incorporation of global information and helps manage the actions of individual agents, similar to how LLMs could benefit from a coordinating mechanism to avoid conflicts or optimize overall system behavior. The concept of action space factorization is relevant for managing the complexity of multiple LLMs collaborating on a task. This study highlights the importance of sample efficiency in MARL, especially for computationally expensive scenarios which are typical for LLMs. The research also points out the challenge of non-stationarity in multi-agent training, where the changing behavior of one agent can impact the learning of others, a crucial consideration when training multiple interacting LLMs.