Model Based Multi Agent Reinforcement

Model-based multi-agent reinforcement learning (MARL) aims to improve the sample efficiency of training multiple agents to cooperate or compete in complex environments by using learned models of the environment's dynamics. Current research focuses on addressing the challenges posed by the exponential growth of the joint state-action space in multi-agent settings, often employing techniques like modular world models, local model optimization, and value decomposition methods to improve prediction accuracy and reduce compounding errors. These advancements are significant because they enable the application of MARL to more complex real-world problems, such as autonomous driving and strategic game playing, where data collection is expensive and computationally intensive.