Explain RL policy types and modern policy gradients

Q: Explain RL policy types and modern policy gradients

This question evaluates understanding of reinforcement learning policy types and modern policy-gradient techniques (TRPO, PPO, GAE) alongside attention mechanism variants (linear attention, Group‑Query Attention), testing competency in algorithmic trade-offs, stability, bias–variance dynamics, and efficiency considerations.

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Question

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Machine Learning Fundamentals (RL + Attention)

Part A — Reinforcement Learning

Define on-policy vs off-policy learning.
- What makes an algorithm on-policy/off-policy?
- Give examples of each.
Explain TRPO (Trust Region Policy Optimization).
- What problem is it trying to solve compared to vanilla policy gradient?
- What is the role of a KL-divergence “trust region” constraint?
Explain PPO (Proximal Policy Optimization).
- How does PPO approximate TRPO’s trust-region behavior?
- What is the clipped surrogate objective trying to prevent?
Explain GAE (Generalized Advantage Estimation).
- What is the definition of the advantage function?
- How does GAE trade off bias vs variance, and what does the $\lambda$ parameter do?

Part B — Attention Mechanisms

Explain linear attention .
- Why is it called “linear” (in what variable does complexity become linear)?
- What approximation or structural change is made vs. standard softmax attention?
- What information can be lost compared with exact softmax attention?
Explain Group-Query Attention (GQA) .
- What is grouped/shared between heads?
- Why does it help inference efficiency and KV-cache memory?
- What are typical quality/accuracy trade-offs?

Explain RL policy types and modern policy gradients

Quick Overview

Machine Learning Fundamentals (RL + Attention)

Part A — Reinforcement Learning

Part B — Attention Mechanisms

Solution

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