Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm

Abstract

The multi-agent task allocation problem has attracted substantial research interest due to the increasing demand for effective solutions in large-scale and dynamic environments. Despite advancements, traditional algorithms often fall short in optimizing efficiency and adaptability within complex scenarios. To address this shortcoming, we propose a genetic algorithm-enhanced PPO (GAPPO) algorithm, specifically developed to enhance decision-making in challenging task allocation contexts. GAPPO employs a deep reinforcement learning framework, enabling each agent to independently evaluate its surroundings, manage energy resources, and adaptively adjust task allocations in response to evolving conditions. Through iterative refinement, GAPPO achieves balanced task distribution and minimizes energy consumption across diverse configurations. Comprehensive simulations demonstrate that GAPPO consistently outperforms traditional algorithms, resulting in reduced task completion time and heightened energy efficiency. Our findings underscore GAPPO’s potential as a robust solution for real-time multi-agent task allocation.