The recent advancements in multi-agent reinforcement learning (MARL) have significantly focused on enhancing coordination and efficiency in decentralized systems. Researchers are increasingly addressing the challenges of relative over-generalization, where agents' individual rationality leads to suboptimal collective actions. Innovative approaches, such as MaxMax Q-Learning, are being developed to refine state transitions and align more closely with optimal joint policies, thereby improving convergence and sample efficiency. Additionally, the field is witnessing a shift towards more efficient training frameworks that eliminate the need for communication, exemplified by the Shared Pool of Information model, which enhances exploration efficiency by reducing force conflicts among agents. Furthermore, advancements in cooperative tasks, such as grasping and transportation, are being driven by the introduction of robust force representation methods that maintain consistency despite environmental variations. These developments collectively push the boundaries of MARL, making it more applicable to real-world scenarios that require robust and efficient multi-agent coordination.