Automated synthesis of control algorithms from first principles

Abstract

A variety of machine learning techniques have been employed to automatically create control algorithms for autonomous vehicles. Much research has focused on various "black box" approaches, in which the synthesized or learned control algorithms perform well when tested, but are difficult or impossible to analyze and understand. This paper presents the use of the ADATE system to evolve a control algorithm based on a racing car simulator. The system evolved compact and analyzable yet sophisticated control algorithms capable of driving millions of randomly generated tracks at high speeds without ever driving off the road. The approach presented is likely to be applicable to most automatic control problems, given a set of training examples and a suitable software simulator.