Multiple Agent

Multiple agent systems research focuses on designing and analyzing systems where multiple autonomous agents interact and collaborate to achieve shared or individual goals. Current research emphasizes the use of large language models (LLMs) and reinforcement learning (RL), including multi-agent RL (MARL) and model-based RL, to enable agents to learn complex behaviors and coordinate effectively, often within simulated environments. These advancements are driving progress in diverse fields, such as supply chain management, power grid control, and software engineering, by improving efficiency, robustness, and decision-making in complex systems. Furthermore, significant effort is dedicated to addressing challenges like deadlock avoidance, efficient communication, and ensuring fairness and safety in multi-agent interactions.