How can competing agent networks learn optimally?
Diffusion Stochastic Learning Over Adaptive Competing Networks
This paper investigates how two teams of AI agents, each with its own goal, can learn and adapt in a shared environment. It proposes two diffusion-based algorithms (ATC-ITC and ATC-C) that enable agents to learn from their own team's experiences and infer/observe the opposing team's actions, even with limited communication between teams. The algorithms are designed to converge towards a stable solution (Nash Equilibrium) where each team performs optimally given the other's strategy.
For LLM-based multi-agent systems, this research provides a framework for decentralized training and interaction between groups of LLMs. The algorithms could enable LLMs to engage in competitive or cooperative scenarios while operating independently, only sharing information within or between teams as needed by the graph structure. The concept of weak cross-team subgraphs is relevant to scenarios where LLMs have partial or indirect access to other LLMs' outputs, mimicking real-world conditions. The use of constant step-sizes in the algorithms is noteworthy, allowing continuous learning and adaptation in dynamic environments commonly encountered in web applications. Finally, the application to decentralized GAN training demonstrates the potential for efficient, distributed training of competing LLM-based generative models.