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When an RL agent produces a trajectory that performs worse than an expert trajectory, compare the reward function's score on the expert behavior with its score on the agent's behavior. If the expert behavior receives the higher score, focus on improving the RL algorithm; if it does not, the reward function is likely the problem and should be revised.

Checking Reward Quality in Reinforcement Learning

Diagnosing an RL problem with verified rewards

If a model assigns a higher reward to a worse outcome than to a better one, the scoring rule is working as intended.

When Reward Scores Favor the Wrong Outcome

Interpreting _____ in reward verification

Match the trajectories and reward comparison to their meanings.

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.

What It Means When the Human Trajectory Outperforms the Learned One

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.

Autonomous Drone Landing Reward Diagnosis

Question:
In the optimization verification check, when should you conclude that the reinforcement learning method itself needs improvement rather than the reward signal?

Sample answer:
You should focus on improving the reinforcement learning method when the trajectory produced by the human or expert is rewarded more highly than the trajectory produced by the agent.

Key points:
- Compare the expert and agent trajectories
- The expert trajectory receives the higher reward
- This suggests the reward definition is probably acceptable, but the optimizer is not finding the better behavior

Rubric:
The response must state that the expert and agent trajectories are compared and that the expert trajectory gets the higher reward.

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