Model-free reinforcement learning techniques have been successfully used in diverse robotic applications. In this paper, we design and implement the Q-learning algorithm, a widely used model-free algorithm to find the optimal speed control function for a fast moving train on a fixed track. The goal is to allow for the train to learn the fastest speed profile it may use on a track, without derailment. We contrast the performance of the learning algorithm with the performance of the human controlling trying to perform the same task. In order the test the proposed algorithm, a complete hardware and software testbed has been designed and implemented, allowing for the evaluation of the learning models over a physical environment. We conclude that in simple tasks, the performance on humans in speed control is similar to the performance of the reinforcement learning algorithm, but when a more complex track is considered, the proposed reinforcement learning learning models outperforms the humans.