In a typical introductory AI class, the topic of reinforcement learning may be allocated only a few hours of class time. One engaging example of reinforcement learning uses a crawling robot that learns to use its two-degree-of-freedom arm to drag itself forward. Unfortunately, the cost of the required hardware is prohibitively expensive for many departments for what is typically a once-a-semester demonstration. So we decided to port the algorithm to a platform that many departments may already have on hand: the LEGO Mindstorms RCX 2.0. Initially the task seemed relatively straightforward: build a robot base out of LEGO parts and implement the algorithm in the Not Quite C language. However the challenges of designing a robot arm without servos and attempting to trim code down to a size that would fit on the RCX has proven to be as educational to the undergraduates working on the project as we hope the final product will be to students in AI classes. This paper describes the challenges we have faced and the solutions we have implemented, as well as the work that remains to be completed.