Models of the world can take many shapes. In this paper, we will discuss how groups of autonomous robots learn languages that can be used as a means for modeling the environment. The robots have already learned simple languages for communication of task instructions. These languages are adaptable under changing situations; i.e. once the robots learn a language, they are able to learn new concepts and update old concepts. In this prior work, reinforcement ]earning using a human instructor provides the motivation for communication. In current work, the world wiU be the motivation for learning languages. Since the languages are grounded in the world, they can be used to talk about the world; in effect, the language is the means the robots use to model the world. This paper will explore the issues of learning to communicate solely through environment motivation. Additionally, we will discuss the possible uses of these languages for interacting with the world.