exploration exploitation

Bandits optimize while they learn, shifting traffic toward winners mid-experiment instead of fixing it like an A/B test. The signal is the explore-exploit tradeoff, the three core algorithms, and knowing exactly when a bandit beats a clean A/B test.

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