Interview: 'your DQN agent isn't learning — how do you debug?'
hardAnswer
- (1) Verify env: hardcoded oracle policy should score well.
- (2) Check reward signal: is it too sparse / too noisy?
- (3) Value function sanity: init Q at 0, after N steps Q should reflect near-term reward magnitude.
- (4) Exploration: is ε schedule too aggressive?
- Are all actions being tried?
- (5) Replay buffer: enough diversity?
- (6) Target network: updating too fast / slow?
- (7) Overestimation: try Double DQN.
- (8) Gradient stability: check norms + reduce LR.
- (9) Compare against baseline (random, SL BC).
Check yourself — multiple choice
- Random
- Verify env + reward signal + Q init sanity + ε schedule + replay diversity + target net update + Double DQN + gradient norms + baseline comparison — step-by-step debug ladder
- Just retrain
- Not real
DQN debug: env + reward + Q sanity + ε + replay + target + Double + grads + baseline.
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Practise Reinforcement Learning
214 interview questions in this topic.