An application of the temporal difference algorithm to the truck backer-upper problem

We use a reinforcement learning approach to learn a real world control problem, the truck backer-upper problem. In this problem, a tractor trailer truck must be backed into a loading dock from an arbitrary location and orientation. Our approach uses the temporal difference algorithm using a neural network as the value function approximator. The novelty of this work is the simplicity of our implementation, yet it is able to successfully back the truck into the loading dock from random initial locations and orientations.