Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual network testbeds such as GENI have emerged, allowing testing of a networked system in controlled yet realistic environments. Nevertheless, setting up those experiments first, and integrate machine learning models later to these deployments is a challenge. In this thesis, we propose the design and implementation of a web and command line tool that integrate Reinforcement Learning (RL) with a virtual network experiment using resources acquired within the GENI testbed.