NVIDIASeptember 2026
Interview question
Describe a problem you solved with reinforcement learning. What did you contribute, and why was reinforcement learning appropriate?
Follow-up questions
- Which reinforcement-learning algorithms or policy implementations did you use?
- What difficulties did you encounter?
- What would you do if the agent becomes stuck in a local maximum?
- If your approach alternates exploration and exploitation, how do you ensure convergence and exploitation at the end of training?
- Does learning continue online during execution, or is the model trained offline?
- With an offline-trained policy network, how would you change its behavior during inference?
- How did you choose the threshold for switching to exploration, determine sufficient training, and evaluate whether the policy was overfitting?