When robots have limited time to practice, you can use optimization to compute exactly which skills to learn and how long to spend on each—trading off learning difficulty against the reward value each skill unlocks.
This paper tackles how robots can learn skills efficiently when practice time is limited. The authors propose Deliberate Practice, an algorithm that decides which skills to practice and for how long to maximize task performance within a fixed budget. They use a mathematical optimization approach to find the best allocation of practice time across different skills.