Multi-Armed Bandit Explorer - Interactive Visualization

The multi-armed bandit problem formalizes the exploration-exploitation tradeoff: you have N options with unknown reward rates, and must decide each round whether to try a new option or exploit the best one you know. Epsilon-greedy explores randomly with probability ε. UCB1 adds a confidence bonus to uncertain arms - arms with fewer pulls get a higher score. Thompson Sampling maintains a Beta distribution over each arm's true reward rate and samples from it, achieving near-optimal regret with no hyperparameter tuning.

• Watch ε-greedy balance random exploration against greedy exploitation - set ε=0 for pure greedy, ε=1 for pure random
• See UCB1 pull uncertain arms first, then converge to the best arm as confidence intervals shrink
• Observe Thompson Sampling probabilistically explore proportional to uncertainty - it naturally reduces exploration as data accumulates
• Track cumulative regret (how much reward was missed vs always picking the best arm) across all three strategies

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