Multi-Agent Collaborative Learning with Dynamic Topology Optimization

Abstract

This paper addresses the challenge of optimizing multi-agent collaborative learning (MCL) by introducing a dynamic topology structure optimization framework. Traditional MCL methods often rely on fixed topologies, which may not be optimal for varying task demands and evolving agent states. We propose a novel approach that leverages reinforcement learning (RL) to dynamically adjust the topology of a multi-agent system, enhancing both efficiency and overall learning performance. The core idea is to model the agent-agent communication and task dependencies as a graph and use RL to learn optimal edge weights and connectivity patterns within this graph. The system adapts to changing conditions by modifying the strengths of connections between agents, adjusting communication frequencies, and potentially adding or removing connections entirely. This dynamic adjustment enables the system to focus computational resources on critical interactions and effectively distribute tasks, ultimately leading to improved convergence rates and better solutions. We outline the key components of the framework, including the state representation, action space, reward function, and the RL algorithm employed. Experimental results (simulated) demonstrate the effectiveness of the proposed approach compared to static topology MCL.