Mathematical Framework

At each time step $t$, you choose an action $a_t$ and receive reward $r_t$. The goal is to maximize cumulative reward over time.

Key Concepts:

• Action Value: $q^*(a) = \mathbb{E}[r_t | a_t = a]$ - true expected reward
• Estimated Value: $Q_t(a) = \frac{1}{N_t(a)} \sum_{i=1}^t r_i \mathbf{1}_{a_i = a}$ - sample average
• Regret: $L_t = \sum_{i=1}^t (q^*(a^*) - q^*(a_i))$ - cumulative opportunity cost

Epsilon-Greedy Algorithm:

Where $\epsilon \in [0,1]$ controls the exploration rate:

• $\epsilon = 0$: Pure exploitation (greedy)
• $\epsilon = 1$: Pure exploration (random)
• $\epsilon = 0.1$: 90% exploitation, 10% exploration

Why it works: As $t \to \infty$, we explore all actions infinitely often, so $Q_t(a) \to q^*(a)$ and the algorithm finds the optimal action.

How to Use This Demo

Interactive Learning:

• Pull arms - Click the "Pull Arm" buttons to try different arms (50 pulls total)
• Watch statistics - Observe total reward, number of pulls, and mean reward for each arm
• Develop strategy - Try to maximize your total reward
• Reset game - Start over with the same arm distributions

Algorithm Demonstrations:

• "Show Exploration Phase" - Watch how epsilon-greedy learns the arm values
• "Show Trained Performance" - See how the algorithm performs after learning
• "Reveal True Distributions" - Discover the actual reward probabilities

Visual Guide:

• Total Reward: Sum of all rewards received from this arm
• Number of Pulls: How many times you've tried this arm
• Mean Reward: Average reward per pull (estimated value)

Strategy and Insights

Human vs Algorithm:

• Try playing first, then compare with the epsilon-greedy algorithm
• Humans often explore too little ("hot hand fallacy") or too much (overthinking)
• The algorithm balances exploration and exploitation mathematically

Key Insights:

• Early exploration matters - Don't commit too quickly to one arm
• Sample size affects confidence - More pulls give better estimates
• Opportunity cost is real - Every suboptimal choice costs potential reward
• Perfect information is impossible - You must act with uncertainty

Real-world Applications:

• Start with some exploration to gather data
• Gradually shift toward exploitation as confidence grows
• Consider the cost of exploration vs potential gains
• Monitor for changes in the environment (non-stationary bandits)

Advanced Concepts:

• Upper Confidence Bound (UCB) - More sophisticated than epsilon-greedy
• Thompson Sampling - Bayesian approach to exploration
• Contextual Bandits - Actions depend on observed context
• Non-stationary Bandits - Reward distributions change over time

Live Competition Mode

Challenge: Maximize your total reward over 100 pulls and compete against your classmates!

Keep this tab active - switching to another tab may temporarily remove you from the leaderboard

Competition Rules:

• You have exactly 100 pulls to maximize your reward
• The arm distributions are different from practice mode
• Your score is submitted automatically after all 100 pulls
• One-time only: Your first completion counts - no retries
• Real-time leaderboard shows top performers

Strategy Hints:

• Balance exploration (trying different arms) with exploitation (using best-known arm)
• Early exploration helps you find the best arm faster
• Don't spend too many pulls exploring - you need to exploit to win!

Scoring:

• Each arm has a hidden probability distribution
• Your total reward is the sum of all rewards received
• Higher total reward = better rank on the leaderboard