Bandit Problem

The multi-armed bandit problem is a sequential decision-making framework where an agent aims to maximize cumulative reward by strategically selecting actions (arms) with uncertain payoffs. Current research emphasizes efficient algorithms for various settings, including contextual bandits (using neural networks to model reward functions), batched bandits (optimizing for limited feedback), and those with non-stationary rewards or adversarial environments. These advancements are driving improvements in online recommendation systems, clinical trials, and other applications requiring adaptive learning under uncertainty, with a strong focus on minimizing regret (the difference between optimal and achieved reward).