Multi-agent multi-armed bandits (MA-MAB) extend the classic MAB problem to scenarios with multiple interacting agents, each making sequential decisions to maximize individual or collective rewards. It's crucial for understanding decentralized decision-making and resource allocation in dynamic environments.
Multi-agent multi-armed bandits (MA-MAB) model situations where multiple decision-makers learn and act in uncertain environments. Recent research highlights the importance of 'procedural fairness' in these systems, ensuring all agents have an equal say in decisions, rather than just focusing on fair outcomes.
MA-MAB, Cooperative MAB, Decentralized MAB, Adversarial MAB (multi-agent)
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