Evaluative feedback as the basis for behavior optimization in the of autonomous vehicle steering

Steering an autonomous vehicle requires the permanent adaptation of behavior in relationship to the various situations the vehicle is in. This paper describes a research which implements such adaptation and optimization based on reinforcement learning (RL) which in detail purely learns from evaluative feedback in contrast to instructive feedback. In this way it self-explores and self-optimises actions for situations in a defined environment. The target of this research is to determine to what extent RL-based systems serve as an enhancement or even an alternative to classical concepts of autonomous intelligent vehicles such as modelling or neural nets.