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Explain PPO and Transformer basics

XPengMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenMachine Learning

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This question evaluates mastery of reinforcement learning algorithms (PPO, policy gradients, TRPO), value-function theory and actor–critic methods (Bellman equations), on- versus off-policy trade-offs and training-stability techniques (clipping, entropy, advantage estimation), plus sequence-modeling fundamentals with Transformers (self-attention, positional encodings and complexity comparisons). It is commonly asked to judge reasoning about algorithmic trade-offs, sample efficiency and stability in practical ML systems, and falls under the Machine Learning domain—specifically reinforcement learning and deep learning/sequence modeling—testing both conceptual understanding and practical application.

Explain the main advantages of Proximal Policy Optimization (PPO) over vanilla policy gradients and TRPO. Write and interpret the Bellman equation for value functions and discuss how it is used in actor–critic methods. Contrast on-policy and off-policy learning, including data reuse, stability, and sample efficiency; where does PPO fit and why? Derive or describe the clipped surrogate objective in PPO, and explain the roles of clipping, entropy bonus, advantage normalization, and GAE in training stability. Finally, cover Transformer basics: how self-attention works, positional encodings, computational complexity vs. RNNs/CNNs, and when you might prefer Transformers in RL or sequence modeling.