How can I efficiently migrate AI agents in a resource-constrained network?
TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration
This paper proposes a system for efficiently migrating AI agent twins (VEAATS) between resource-constrained devices (like in-vehicle systems and roadside units) in a vehicular network. It uses a multi-leader multi-follower Stackelberg game to model the interactions and incentives between these devices, optimizing resource allocation during migration. A lightweight, multi-agent deep reinforcement learning algorithm (TinyMA-IEI-PPO) is developed, incorporating exploration incentives and a dynamic pruning method to reduce computational demands while maintaining performance close to the theoretical equilibrium.
Key points for LLM-based multi-agent systems: The framework employs a combination of game theory and a tiny multi-agent deep reinforcement learning algorithm, demonstrating a practical approach to manage complex interactions and resource allocation in a distributed LLM-agent environment. The emphasis on lightweight algorithms and dynamic pruning addresses the computational challenges inherent in deploying LLM-based agents on resource-constrained devices, crucial for real-world web applications. The use of exploration incentives ensures agents explore globally impactful actions, which is relevant for coordinated behavior in multi-agent LLM systems. This work could inform the development of efficient, distributed, and scalable multi-agent web applications powered by LLMs.