How to train agents in decentralized games for approximate Nash equilibria?
Decentralized Learning in General-sum Markov Games
September 10, 2024
https://arxiv.org/pdf/2409.04613- The paper proposes a new method for decentralized learning in multi-agent systems, particularly in scenarios where agents have different goals (general-sum Markov games).
- This method uses a novel "Markov Near-Potential Function" (MNPF) to analyze and guide the learning process of independent agents towards a stable outcome (approximate Nash equilibrium).
- While not directly addressing LLMs, the concepts of decentralized learning and achieving stable outcomes in multi-agent systems directly apply to LLM-based multi-agent development.
- The MNPF could potentially be adapted for LLM-based agents to analyze and improve their interactions within a shared environment.