Can AI agents learn to be fair while being efficient?
Cooperation and Fairness in Multi-Agent Reinforcement Learning
This research paper tackles the challenge of achieving fairness in multi-agent navigation, ensuring no agent is unfairly burdened with excessive travel distance. The authors leverage the concept of min-max fairness during training, assigning goals to agents in a way that minimizes the maximum distance traveled by any single agent. This approach proves particularly relevant to LLM-based multi-agent systems as it allows for decentralized goal assignments based on local observations, removing reliance on a central authority for decision-making. The study highlights that fairness can be achieved without significantly sacrificing efficiency, making it a promising avenue for developing collaborative LLM agents operating in resource-constrained environments.