Model other agents in simulation
Company: Tesla
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: hard
Interview Round: Technical Screen
## Scenario
You are building a driving simulation environment for training/evaluating an autonomous agent (planning or RL). Besides the ego vehicle, the simulator must include other traffic participants (“agents”) such as cars, pedestrians, cyclists.
## Question
1. How would you **model the behavior of other agents** in simulation so that ego training is realistic and robust?
2. What are the **main approaches** (e.g., log replay, rule-based, learned policies), and what are the tradeoffs in **realism, controllability, safety, and distribution shift**?
3. How would you **validate** that your agent-modeling strategy is “good enough” for downstream training and evaluation?
Quick Answer: This question evaluates a candidate's competency in modeling other agents within simulation environments, focusing on designing agent behavior models and assessing their impact on training and evaluation robustness.