  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.
