Contextual Combinatorial

Contextual combinatorial bandits address the challenge of selecting optimal subsets of actions from a large space, considering both individual action characteristics and contextual information. Current research focuses on developing efficient algorithms, such as UCB and Thompson Sampling variants, often incorporating neural networks for complex reward functions and addressing issues like asynchronous communication and heterogeneous user behavior in federated learning settings. These advancements are improving the performance of recommendation systems, negotiation strategies, and network optimization, among other applications, by enabling more effective exploration and exploitation in complex decision-making scenarios. The field is also exploring fairness considerations and handling various feedback mechanisms, including partial observations and probabilistically triggered arms.