Multi Agent Multi Armed Bandit

Multi-agent multi-armed bandits (MAMAB) study how multiple agents collaboratively learn optimal actions in environments with uncertain rewards, aiming to minimize overall regret (the difference between achieved and optimal rewards). Current research focuses on addressing challenges like heterogeneous agents with varying reward sensitivities, decentralized decision-making in resource-constrained settings (e.g., mobile edge computing, blockchain networks), and the impact of malicious agents or communication limitations. These advancements have implications for various applications, including resource allocation, smart grids, and collaborative AI systems, by providing efficient and robust algorithms for distributed decision-making under uncertainty.