Reinforcement learning is designed to solve tasks which require complex sequential decision making. Learning to control and drive an autonomous vehicle is one such complex problem. In this workshop I present a somewhat simplified version of the problem with a simulation of a vehicle. You can use this simulation to train an agent to drive a car. The coolest part of this experiment is the use of a variational auto-encoder to build a model of the world from experimental data.