Researchers Develop Algorithm for Optimal Decision Making Under Heavy-tailed Noisy Rewards
By Adedapo Adesanya The exploration algorithms for stochastic multi-armed bandits (MABs)–sequential decision-making problems under uncertain environments–typically assume light-tailed distributions for reward noises. However, real-world
