How can bottom-up reputation improve multi-agent cooperation?
Bottom-Up Reputation Promotes Cooperation with Multi-Agent Reinforcement Learning
This paper introduces LR2, a novel multi-agent reinforcement learning method that promotes cooperation among agents by enabling them to learn and assign reputations to each other. Unlike traditional methods relying on predefined social norms, LR2 allows agents to develop their own evaluation policies, leading to a bottom-up, decentralized system for reputation management.
For LLM-based multi-agent systems, LR2 offers a promising way to foster cooperation without relying on centrally imposed rules. Its decentralized nature aligns with the distributed nature of many LLM applications. The ability to learn dynamic reputations can be particularly useful in complex scenarios where predefined norms are difficult to define or enforce. The paper also highlights the importance of considering the spatial distribution of agents and the impact of adversarial actors in such systems.