Communication is a vital activity in the development of information exchange. However, the means by which communication might have emerged and become understood among agents is not yet understood. In this project, I used the framework of reinforcement learning and computation to understand how communication and signaling could emerge among multiple adaptive agents, also how agents exploit communication. The idea of having a group of agents to learn how to accomplish a task via communication, rather than explicitly how to communicate, is important in understanding the emergence of communication and language. Although previous works discussed some specific aspects of emergence of communication and language among robots, not until recently researchers start to try methods of reinforcement learning, which is a general framework for an agent to learn policies to improve its behavior. I investigated the emergence of communication among reinforcement learning learners using an “entering the same room” task in the grid world. By learning to communicate, agents are able to generate the meaning of signaling to achieve a better performance via corporation.