In this article, we study cooperative multiagent systems (MASs) with multiple tasks by using reinforcement learning (RL)-based algorithms. The target for a single-agent RL system is represented by its scalar reward signals. However, for an MAS with multiple cooperative tasks, the holistic reward signal consists of multiple parts to represent the tasks, which makes the problem complicated. Existing multiagent RL algorithms search distributed policies with holistic reward signals directly, making it difficult to obtain an optimal policy for each task. This article provides efficient learning-based algorithms such that each agent can learn a joint optimal policy to accomplish these multiple tasks cooperatively with other agents. The main idea of the algorithms is to decompose the holistic reward signal for each agent into multiple parts according to the subtasks, and then the proposed algorithms learn multiple value functions with the decomposed reward signals and update the policy with the sum of distributed value functions. In addition, this article presents a theoretical analysis of the proposed approach. Finally, the simulation results for both discrete decision-making and continuous control problems have demonstrated the effectiveness of the proposed algorithms.

中文翻译：

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协作多Agent系统的任务分解强化学习。

在本文中，我们通过使用基于强化学习（RL）的算法来研究具有多个任务的协作式多代理系统（MAS）。单代理RL系统的目标由其标量奖励信号表示。但是，对于具有多个协作任务的MAS，整体奖励信号由代表任务的多个部分组成，这使问题变得复杂。现有的多主体RL算法直接使用整体奖励信号搜索分布式策略，因此很难为每个任务获得最佳策略。本文提供了有效的基于学习的算法，以便每个代理可以学习联合的最佳策略以与其他代理协作完成这些多个任务。该算法的主要思想是根据子任务将每个智能体的整体奖励信号分解为多个部分，然后提出的算法利用分解后的奖励信号学习多个价值函数，并利用分配的价值函数之和来更新策略。此外，本文还对所提出的方法进行了理论分析。最后，针对离散决策和连续控制问题的仿真结果证明了所提算法的有效性。