  Increasing complexity of modern chips makes design validation more difficult.
Existing approaches are not able anymore to cope with the complexity of tasks
such as robust performance tuning in post-silicon validation. Therefore, we
propose a novel approach based on learn-to-optimize and reinforcement learning
in order to solve complex and mixed-type tuning tasks in a efficient and robust
way.
