Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. Several recent works have focused specifically on the study of communication approaches in MARL. While multiple communication methods have been proposed, these might still be too complex and not easily transferable to more practical contexts. One of the reasons for that is due to the use of the famous parameter sharing trick. In this paper, we investigate how independent learners in MARL that do not share parameters can communicate. We demonstrate that this setting might incur into some problems, to which we propose a new learning scheme as a solution. Our results show that, despite the challenges, independent agents can still learn communication strategies following our method. Additionally, we use this method to investigate how communication in MARL is affected by different network capacities, both for sharing and not sharing parameters. We observe that communication may not always be needed and that the chosen agent network sizes need to be considered when used together with communication in order to achieve efficient learning.

翻译：多智能体强化学习（MARL）是多智能体系统领域中一个广泛的研究方向。近期多项工作专门聚焦于MARL中通信方法的研究。尽管已提出多种通信方法，但这些方法仍可能过于复杂且难以迁移到更实际的场景中，其中一个原因在于广泛使用的参数共享技巧。本文探讨了不共享参数的独立学习智能体如何在MARL中实现通信。我们证明该设置可能引发若干问题，并为此提出一种新的学习方案作为解决方案。结果表明，尽管存在挑战，独立智能体仍可依据我们的方法习得通信策略。此外，我们利用该方法研究了不同网络容量（包括共享参数与不共享参数两种情况）对MARL中通信效果的影响。实验发现，通信并非总是必要，且为实现高效学习，选择智能体网络大小时需结合通信机制加以考量。