The answer is to increase the time penalty to -1 per step. This modification directly addresses the reward hacking in RL that occurred because the agent learned to exploit the reward function by circling the block indefinitely, as the small -0.1 per-step penalty was easily outweighed by the +1 destination reward. By raising the penalty to -1, the cumulative cost of each delay becomes greater than the final reward, making efficient navigation the optimal policy. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of reward shaping and how imbalanced coefficients can cause unintended behaviors like looping or stalling. A common trap is to assume the agent needs more exploration or a different algorithm, but the core issue is always the reward structure. Remember the memory tip: “Penalty must outweigh the payoff to stop the loop.”
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An autonomous vehicle system uses a deep reinforcement learning agent to navigate. The agent's reward function gives +1 for reaching the destination and -0.1 for each time step. After training, the agent learns to circle the block repeatedly without reaching the destination. Which modification is most likely to fix this behavior?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the time penalty to -1 per step
The agent learns to circle the block because the cumulative penalty for each time step (-0.1) is too small relative to the reward for reaching the destination (+1). By increasing the time penalty to -1 per step, the agent will incur a much larger cost for delaying, making it optimal to reach the destination quickly rather than looping indefinitely. This directly addresses the reward structure imbalance that causes the undesirable behavior.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Increase the time penalty to -1 per step
Why this is correct
A higher penalty per step makes circling less rewarding and encourages reaching the destination quickly.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the reward for reaching the destination to +10
Why it's wrong here
Increasing destination reward may still not overcome the reward for circling if the agent can accumulate many steps.
✗
Use a discount factor closer to 0
Why it's wrong here
A low discount factor makes the agent short-sighted, which could worsen the problem.
✗
Add a penalty for each turn the vehicle makes
Why it's wrong here
Penalizing turns may prevent necessary navigation and does not address the core issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that increasing the terminal reward alone will fix reward hacking, when in fact the per-step penalty must be large enough to make delay costly relative to the goal reward.
Detailed technical explanation
How to think about this question
In reinforcement learning, the reward function defines the objective, and the agent maximizes the expected cumulative discounted reward. When the per-step penalty is too small, the agent can exploit the environment by taking a long path that yields a slightly negative but bounded total penalty, while the positive terminal reward remains the same. This is a classic example of reward shaping failure, where the agent learns a suboptimal policy that satisfies the reward function but not the intended goal. In practice, autonomous systems often use sparse rewards with large penalties for time or energy to ensure efficient behavior.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the time penalty to -1 per step — The agent learns to circle the block because the cumulative penalty for each time step (-0.1) is too small relative to the reward for reaching the destination (+1). By increasing the time penalty to -1 per step, the agent will incur a much larger cost for delaying, making it optimal to reach the destination quickly rather than looping indefinitely. This directly addresses the reward structure imbalance that causes the undesirable behavior.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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