Cooperative multi-agent systems are of current interest due to their relevance to both robotics and networking. Researchers often create machine learning environments to explore these domains; however, the lack of reuse of previous environments prevents comparisons between the works of research groups. Further, while a growing understanding of the relationships between problem domains and machine learning techniques has emerged over time, there have been few attempts to systematically measure the performance of machine learning techniques as domain characteristics vary. Our primary goal is to create TASER, the Team Agent Simulator for Efficient Research—a nontrivial simulation system aimed at the efficient comparison of machine learning algorithms given a wide variety of conditions, concentrating on cooperative tasks. Our secondary goal is the adoption of TASER by other researchers.