Cumulative Reward

Cumulative reward, the total reward accumulated over time in sequential decision-making problems, is a central concept in reinforcement learning and related fields. Research focuses on maximizing cumulative reward under various constraints, including uncertainty, strategic agents, and fairness considerations, often employing models like multi-armed bandits and Kalman filters, along with algorithms such as UCB and variations of value iteration. These advancements have implications for diverse applications, from recommender systems and resource allocation to safety-critical domains requiring robust and certifiably reliable policies. The ongoing emphasis is on developing algorithms that are both efficient and guarantee performance, while also addressing issues of fairness and robustness.