Exponential Family Reward

Exponential family reward models are used in sequential decision-making problems, such as multi-armed bandits, to analyze scenarios where rewards follow distributions from the exponential family (including Bernoulli, Gaussian, and exponential distributions). Current research focuses on developing and analyzing efficient algorithms, like Thompson Sampling variants (e.g., ExpTS and its improvements), to optimize reward accumulation while minimizing regret (the difference between obtained and optimal rewards). These advancements aim to improve the performance of adaptive experimental designs and resource allocation strategies across various fields, leading to more efficient and effective decision-making in applications ranging from clinical trials to online advertising.