exploration exploitation

A bandit learns and optimizes at the same time, moving traffic toward the winning arm during the experiment rather than holding the split fixed like an A/B test. Interviewers look for the explore-exploit tradeoff, the three core algorithms, and a clear sense of when a bandit wins over a clean A/B test.

RL sits under RLHF, robotics, and recommendation, and interviewers want the core framing. What matters is the agent-environment-reward loop and the three things that make it harder than supervised learning. Here is the answer.