How can I improve multi-agent LLMs via dynamic agent replacement?
Free Agent in Agent-Based Mixture-of-Experts Generative AI Framework
This paper introduces the Reinforcement Learning Free Agent (RLFA) algorithm, a method for dynamically upgrading agents within multi-agent AI systems. Inspired by free agency in sports, underperforming agents are automatically replaced by better candidates from a pool of available agents. This approach is combined with a Mixture-of-Experts (MoE) architecture, where each agent uses specialized sub-models for different tasks. This combination aims to create more adaptable, higher-performing multi-agent systems, particularly relevant for complex tasks like fraud detection in constantly evolving environments. The RLFA algorithm uses a reward system and performance thresholds to manage agent lifecycles, ensuring continuous improvement and adaptation within the multi-agent system. Key advantages for LLM-based systems include higher accuracy, faster adaptation to new tasks and challenges, and improved robustness against emerging threats.