Multi Objective Multi Armed Bandit

Multi-objective multi-armed bandits (MOMAB) address the challenge of selecting the best action from a set when multiple, potentially conflicting, objectives need to be optimized simultaneously. Current research focuses on developing efficient algorithms, such as those based on upper confidence bounds (UCB) and scalarization techniques (including hypervolume scalarizations), to identify Pareto optimal solutions – those that cannot be improved in one objective without sacrificing performance in another. These advancements are crucial for tackling real-world problems involving trade-offs, such as resource allocation in wireless networks or optimizing vaccination strategies, where robust and efficient decision-making under uncertainty is paramount. The field is also actively exploring robust methods to handle noisy or adversarial data, ensuring reliable performance in less-than-ideal conditions.