Senior interview questionAsked at Tesla · interview write-up
You are training an RL agent to drive: compare policy optimization methods and design a reward that respects speed limits without exploitable failure modes.
Model answer
PPO is stable and simple on-policy; SAC is off-policy and more sample-efficient, which matters when simulation is expensive.
Reward progress along the route, penalise speed above the limit with a penalty that grows with the excess, and add small penalties for jerk and harsh braking. Make collisions and red lights terminations or hard constraints, for example via a Lagrangian method, not weights the agent can trade away. Against exploits: reward route progress, not raw speed, so circling earns nothing; bound per-step rewards; and inspect rollouts for reward hacking in adversarial simulated scenarios.
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