This repository offers fine-tuned implementations of cooperative multi-agent reinforcement learning algorithms including QMIX, VDN, and MAPPO variants. It focuses on StarCraft Multi-agent Challenge (SMAC) as the primary benchmark, incorporating implementation tricks such as value function clipping, orthogonal initialization, n-step returns, and large batch training. The project also supports Google Football environments as an alternative multi-agent task.