Many Arm

Many-armed bandit problems address the challenge of sequentially selecting the best option (arm) from a large set, maximizing cumulative reward while minimizing exploration costs. Current research focuses on refining algorithms like UCB (Upper Confidence Bound) and developing novel approaches such as regularized and clustered assignment forests, particularly for scenarios with similar arms or resource sharing among multiple agents. These advancements are crucial for optimizing personalized treatments, improving recommender systems, and accelerating cold-start learning in applications like online advertising and product recommendations, where the number of options is vast and constantly evolving.