How can I learn multi-agent utility functions robustly?
DISTRIBUTIONALLY ROBUST INVERSE REINFORCEMENT LEARNING FOR IDENTIFYING MULTI-AGENT COORDINATED SENSING
September 24, 2024
https://arxiv.org/pdf/2409.14542This research paper introduces a robust algorithm to determine if individual agents within a multi-agent system are acting in a coordinated way (making decisions consistent with overall optimal group behavior) and to reconstruct the underlying objectives guiding each agent's actions. This is particularly relevant to LLM-based multi-agent systems, as it provides a method to analyze whether LLMs within a system are working together effectively and to understand the individual goals each LLM is pursuing. The algorithm is designed to handle noisy real-world data and focuses on worst-case performance, making it suitable for analyzing complex interactions within multi-agent LLM applications.