How can LLMs learn fair, diverse, and creative strategies in multi-agent games?
CONVEX MARKOV GAMES: A FRAMEWORK FOR FAIRNESS, IMITATION, AND CREATIVITY IN MULTI-AGENT LEARNING
October 23, 2024
https://arxiv.org/pdf/2410.16600This research paper introduces Convex Markov Games (cMGs), a framework for multi-agent reinforcement learning that allows for more complex and realistic agent preferences beyond simple reward maximization.
For LLM-based multi-agent systems, cMGs offer a way to:
- Guide agents toward desirable behavior: Encourage creativity (exploring diverse solutions), imitation (learning from demonstrations), and fairness (achieving equitable outcomes) by incorporating these elements directly into the agents' utility functions.
- Find stable solutions efficiently: The paper proves the existence of stable equilibrium points in cMGs and presents a practical algorithm for finding them. This means LLMs can be trained to reach agreements and coordinate effectively.
- Design for complex, realistic scenarios: cMGs offer a more expressive way to model how LLMs interact in collaborative or competitive settings, leading to more nuanced and robust multi-agent applications.