Question: In a reinforcement learning verification test, suppose the score of a human-crafted trajectory is higher than the score of the trajectory produced by the agent. What does that imply about the reward function and the learning procedure?

Sample answer: If the human-crafted trajectory receives the higher score, the reward function is behaving sensibly: it is preferring the better behavior over the worse one. That suggests the reward model is not the main source of the failure. Instead, the problem is likely in the reinforcement learning method, which is not finding the high-scoring behavior even though the reward function distinguishes it correctly. In that case, the next step is to improve the optimization or exploration strategy used by the agent.

Key points:

• Shows the reward function ranks the better trajectory higher
• Points to the learning algorithm as the likely failure point
• Indicates the agent is not successfully maximizing the reward

Rubric: A strong answer should explain that the inequality supports the reward function and shifts attention to the reinforcement learning algorithm as the cause of poor performance.