Multi Agent Sequential Decision

Multi-agent sequential decision-making studies how multiple agents make a series of decisions over time, interacting within a shared environment to achieve individual or collective goals. Current research emphasizes developing algorithms that address challenges like exploration-exploitation trade-offs, privacy preservation of agent preferences, and ensuring fairness in long-term outcomes, often employing models such as Markov Decision Processes (MDPs) and Markov Games. These advancements have implications for diverse fields, including resource allocation, robotics, and human-AI interaction, by enabling more efficient, ethical, and robust systems.