Submodular Reward

Submodular reward functions model scenarios where the value of adding an item to a set diminishes as the set grows, reflecting diminishing returns common in many real-world problems. Current research focuses on developing efficient algorithms, such as greedy approaches and variations of policy gradient methods, to optimize decision-making under these submodular reward structures in various settings, including multi-armed bandits, reinforcement learning, and multi-agent systems. These advancements are improving the scalability and efficiency of solving complex optimization problems in areas like resource allocation, influence maximization, and experimental design. The resulting algorithms offer improved performance and sample efficiency compared to traditional additive reward models.