Advanced Reinforcement Learning Interview Questions, issue 14, Feb 9, 2026
The Local Randomness Trap
Senior RL Research Scientist interview at Google DeepMind, and the interviewer asks:
“We’re training an agent for a sparse-reward, long-horizon task. Your Epsilon-Greedy agent is flatlining and stuck in local optima. However, a Thompson Sampling agent solves it efficiently. Why? What is the fundamental difference in how they treat uncertainty?”
Candidates miss that epsilon-greedy explores actions, not hypotheses, making long-horizon discovery statistically impossible under sparse rewards.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.