Multi-agent communication has been traditionally used as a computational tool to study language evolution. Recently, it has attracted attention also as a means to achieve better coordination among multiple interacting agents in complex environments. However, is it easy to scale previous research in the new deep learning era? In this talk, I will first give a brief overview of some of the previous approaches that study emergent communication in cases where agents are given as input symbolic data. I will then move on to presenting some of the challenges that agents face when are placed in grounded environments where they receive raw perceptual information and how environmental or pre-linguistic conditions affect the nature of the communication protocols that they learn. Finally, I will discuss some potential remedies that are inspired from human language and communication.