Explain PPO and Transformer basics

Q: Explain PPO and Transformer basics

This is a Machine Learning interview question from XPeng for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

PPO, Bellman Equations, On-/Off-Policy Learning, and Transformer Basics

Context: You are interviewing for a machine learning role with emphasis on reinforcement learning and sequence modeling. Answer the following concisely but completely, using formulas where helpful.

Tasks

PPO vs. vanilla policy gradients and TRPO

Explain the main advantages of Proximal Policy Optimization (PPO) over vanilla policy gradients and Trust Region Policy Optimization (TRPO).

Bellman equations and actor–critic

Write and interpret the Bellman equation for value functions (for V and Q).
Explain how the Bellman equation is used in actor–critic methods.

On-policy vs. off-policy

Contrast on-policy and off-policy learning in terms of data reuse, stability, and sample efficiency.
State where PPO fits and why.

PPO objectives and stability components

Derive or describe the clipped surrogate objective in PPO.
Explain the roles of clipping, entropy bonus, advantage normalization, and GAE in training stability.

Transformer basics for sequence modeling and RL

Explain how self-attention works (queries, keys, values).
Describe positional encodings.
Compare computational complexity vs. RNNs/CNNs.
Discuss when you might prefer Transformers in RL or sequence modeling.

Explain PPO and Transformer basics

PPO, Bellman Equations, On-/Off-Policy Learning, and Transformer Basics

Tasks

Solution

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