Adversarial Multi Armed Bandit

Adversarial multi-armed bandits (MABs) study online decision-making under worst-case scenarios, aiming to minimize cumulative losses when facing an adversary that strategically sets rewards/losses. Current research focuses on improving algorithms' robustness to unbounded losses, delayed feedback, and heavy-tailed reward distributions, often employing techniques like exponential weights, online mirror descent, and clipping mechanisms. These advancements are crucial for developing reliable algorithms in applications like online advertising, recommendation systems, and personalized learning, where adversarial conditions and uncertainty are prevalent. The field's impact stems from its ability to provide theoretical guarantees and practical solutions for sequential decision problems in complex, unpredictable environments.