A self-driving car was simulated using The Open Racing Car Simulator (TORCS). It was trained using a reinforcement learning algorithm to be able to autonomously detect its lane, keep driving in it, and maximize its speed using only readings of its several distance sensors together with its speed value as its states. Different hyper-parameters were changed in a previously existing Q-learning algorithm to find the combination that achieves the best results.