  Vibration compensation is important for many domains. For the machine tool
industry it translates to higher machining precision and longer component
lifetime. Current methods for vibration damping have their shortcomings (e.g.
need for accurate dynamic models). In this paper we present a reinforcement
learning based approach to vibration compensation applied to a machine tool
axis. The work describes the problem formulation, the solution, the
implementation and experiments using industrial machine tool hardware and
control system.
