Learning to Drive Small Scale Cars from Scratch

We consider the problem of learning to drive low-cost small scale cars using reinforcement learning. It is challenging to handle the long-tailed distributions of events in the real-world with handcrafted logical rules and reinforcement learning could be a potentially more scalable solution to deal with them. We adopt an existing platform called Donkey car for low-cost repeatable and reproducible research in autonomous driving. We consider the task of learning to drive around a track, given only monocular image observations from an on-board camera. We demonstrate that the soft actor-critic algorithm combined with state representation learning using a variational autoencoder can learn to drive around randomly generated tracks on the Donkey car simulator and a real-world track using the Donkey car platform. Our agent can learn from scratch using sparse and noisy rewards within just 10 minutes of driving experience.