What Are Multi-Armed Bandits and Can You Run Them in LaunchDarkly?

Multi-armed bandit algorithms are a class of algorithms designed to minimize regret when running experiments, such as A/B tests. These algorithms allocate more traffic to the best performing variation as they learn more about the value of each variation. This approach is particularly useful for marketing site changes where maximizing conversions is important. LaunchDarkly supports both automated and non-automated multi-armed bandit implementations, with the latter involving manual adjustment of traffic allocation based on experiment results. The epsilon-greedy algorithm is an example of a multi-armed bandit algorithm that balances exploration and exploitation of variations.

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