This thesis discusses Artificial Economy with applications to Reinforcement Learning. It focuses first on Reinforcement Learning, comparing it to other types of learning such as Supervised Learning. It then covers a historical overview of the field and summarizes the current works. The work described in this thesis has a psychological approach as we focus on reward and punishment. We experiment Artificial Economy with a prototype which solves the Blocks World problem. We then show how the Artificial Economy approach can be integrated with other concepts of Reinforcement Learning techniques.