Highway Pilot Training from Demonstration

As driving task has been and still is the domain of humans, they are a relatively good model of how to behave on road. However, as we would like to constantly raise standards of safety and efficiency of transportation by automated driving, simple imitation of human driving seems not to be good enough. In this paper, we present a method for learning a baseline agent represented as a neural network, which is responsible for planning the behaviour of an automated car, based on a set of expert demonstrations. We show that imitation learning could be a promising way for achieving initial policy, which is almost as effective as human. The generated policy could be then fine-tuned by the reinforcement learning methods. We show that such an approach suits well for an automated driving task and allows faster training of the RL-based artificial driver.