Stochastic Multi Armed Bandit

Stochastic multi-armed bandits (MABs) model sequential decision-making under uncertainty, aiming to maximize cumulative reward by strategically selecting actions (arms) with unknown reward distributions. Current research focuses on improving algorithm efficiency, particularly for heavy-tailed reward distributions and non-stationary environments, often employing variations of Upper Confidence Bound (UCB) algorithms and Thompson Sampling, along with techniques like the median-of-means and optimism in the face of ambiguity. These advancements have implications for various fields, including online advertising, clinical trials, and reinforcement learning, by enabling more efficient and robust exploration-exploitation strategies in complex, real-world scenarios.