How do you design experiments for complex multi-armed bandit problems?

Multi-armed bandit problems balance exploration and exploitation in sequential decision-making with uncertain rewards.\n\n• Algorithms: Epsilon-greedy, Upper Confidence Bound (UCB), Thompson Sampling\n• Contextual bandits: Incorporate user/item features for personalization\n• Evaluation: Regret minimization, cumulative reward optimization\n• Applications: Content recommendation, pricing optimization, clinical trials\n\nExample: Website personalization uses contextual Thompson Sampling to optimize content recommendations, incorporates user demographics and behavior features, and balances exploration of new content with exploitation of known preferences.