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）涵盖了多智能体系统领域内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。尽管已有多种通信方法被提出，但这些方法可能仍过于复杂，且不易迁移至更实际的应用场景。其原因之一在于广泛使用的参数共享技巧。本文研究了不共享参数的独立学习智能体在MARL中如何实现通信。我们证明这种设定可能引发若干问题，并为此提出一种新的学习方案作为解决方案。实验结果表明，尽管存在挑战，独立智能体仍能通过我们的方法学习通信策略。此外，我们利用该方法探究了不同网络容量（包括参数共享与非共享情形）对MARL中通信效果的影响。我们观察到通信并非总是必需的，且在选择与通信机制协同使用的智能体网络规模时需加以考量，以实现高效学习。