关键词: 多智能体系统, 任务分配, 迁移学习, Q学习

Abstract:

To multitask assignment problem, traditional methods search for an optimal solution without considering the relationship among tasks as well as the impact of historical experience to new tasks, which leads to high complexity. This paper studies the issue of multitask allocation in multiagent systems. Transfer learning is employed to accelerate task allocation and execution. When assigning the target task, the most suitable source task is found by calculating the similarity between source tasks and target task. Afterwards, the allocation mode and execution process of source task are transferred to the new task to improve efficiency and save time. Finally, the empirical results reveal that the proposed approach outperforms the existing methods in terms of running time and the average discounted return.

Key words: multi-agent system, task allocation, transfer learning, Q-learning