Case context: You are training a reinforcement-learning controller for a quadrotor that must settle onto a marked pad in simulation. The score combines touchdown accuracy and motion stability. The learned policy succeeds at the landing task, but its path is noticeably more jerky than a skilled operator's reference flight. When you run the optimization verification check, you find that R(reference) is lower than R(policy).

Question: Which part of the system should you revise first, and what does this result tell you?

Sample answer: Revise the reward function first. Since the reference flight gets a lower score than the rougher policy flight, the current scoring rule is favoring an undesirable behavior. That means the objective is not capturing the intended tradeoff between precise landing and smooth motion, so the reward design should be adjusted.

Key points:

• The reward function is the component that needs revision
• The present score prefers the worse trajectory
• The landing-versus-smoothness balance is set incorrectly

Rubric: The response must name the reward function and explain that it is ranking the inferior trajectory above the better one.