Epsilon Greedy

Epsilon Greedy is a simple yet effective exploration-exploitation strategy used in reinforcement learning and multi-armed bandit problems. It balances between exploring new actions to gather information and exploiting known actions to maximize rewards, using a parameter epsilon to control the probability of exploration. This algorithm is foundational for decision-making in uncertain environments, such as online advertising, recommendation systems, and clinical trials.

Also known as: Epsilon-Greedy, ε-greedy, epsilon greedy algorithm, epsilon-greedy policy, epsilon greedy strategy

Why learn Epsilon Greedy?

Developers should learn Epsilon Greedy when building systems that require adaptive decision-making under uncertainty, like A/B testing, dynamic pricing, or game AI. It's particularly useful in scenarios where you need to quickly converge to optimal choices while minimizing regret, as it provides a straightforward way to tune exploration versus exploitation trade-offs. For example, in web applications, it can optimize click-through rates by testing different content variations.

Learning Resources

• book: Reinforcement Learning: An Introduction - Sutton & Barto
• course: Multi-Armed Bandits and Exploration Strategies
• tutorial: Epsilon Greedy Algorithm Tutorial
• video: Exploration vs Exploitation: Epsilon Greedy
• book: Bandit Algorithms Book - Lattimore & Szepesvári