Interview: 'design an RL system for X' — what's your framework?
mediumAnswer
- Structure: (1) Define MDP: state (features observable), action (decision), reward (business KPI), horizon.
- (2) Identify challenges: sparse / delayed rewards, safety, sample efficiency.
- (3) Choose approach: online RL vs offline RL vs BC vs bandit vs supervised.
- (4) Design reward + KL / constraint (avoid hacking).
- (5) Simulation + safety validation before deployment.
- (6) A/B test + monitor drift.
- Show tradeoffs at each step.
Check yourself — multiple choice
- Random
- Frame MDP (state, action, reward, horizon) + challenges (sparse / delayed / safety / sample eff) + approach choice + reward design + sim + safety + A/B monitor drift; show tradeoffs
- Just PPO
- Not real
RL design: MDP framing + challenges + approach + reward + sim + A/B + drift.
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Practise Reinforcement Learning
214 interview questions in this topic.
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