Cooperative Bandit

Cooperative bandit problems address the challenge of multiple agents collaboratively learning optimal strategies in uncertain environments, aiming to minimize collective regret while maximizing overall reward. Current research focuses on developing efficient algorithms, often based on variations of Upper Confidence Bound (UCB) methods, that handle noisy rewards, asynchronous actions, imperfect communication (including delays and corruptions), and fairness considerations within distributed systems. These advancements are significant for applications like Internet of Things (IoT) networks, fog computing, and multi-agent systems where decentralized decision-making and efficient resource allocation are crucial.