Neural Contextual Bandit

Neural contextual bandits are a class of machine learning algorithms that address the challenge of making sequential decisions in environments with uncertain rewards, leveraging contextual information to improve decision-making. Current research focuses on developing efficient and scalable algorithms, often employing neural networks to model complex reward functions and incorporating techniques like Thompson Sampling or Upper Confidence Bound to balance exploration and exploitation. These methods find applications in diverse fields such as personalized recommendations, resource allocation in communication networks, and adaptive control systems, offering significant improvements in performance and efficiency over traditional approaches. A key focus is on reducing computational costs and improving theoretical guarantees for regret, the difference between the algorithm's performance and the optimal strategy.