Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two common design principles: value decomposition and parameter sharing. A typical MARL algorithm of this fashion decomposes a centralized Q-function into local Q-networks with parameters shared across agents. Such an algorithmic paradigm enables centralized training and decentralized execution (CTDE) and leads to efficient learning in practice. Despite all the advantages, we revisit these two principles and show that in certain scenarios, e.g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes. In contrast, policy gradient (PG) methods with individual policies provably converge to an optimal solution in these cases, which partially supports some recent empirical observations that PG can be effective in many MARL testbeds. Inspired by our theoretical analysis, we present practical suggestions on implementing multi-agent PG algorithms for either high rewards or diverse emergent behaviors and empirically validate our findings on a variety of domains, ranging from the simplified matrix and grid-world games to complex benchmarks such as StarCraft Multi-Agent Challenge and Google Research Football. We hope our insights could benefit the community towards developing more general and more powerful MARL algorithms. Check our project website at https://sites.google.com/view/revisiting-marl.

围绕合作多智能体强化学习，实现了依照价值分解及参数共用两大设计原则，其中心的Q函数通过局部化的Q网络在代理间共享参数。然而，我们证明在某些环境中，比如高度多模式的奖励环境下，价值分解以及参数共享会引起问题并导致不良结果。相反，个体策略的策略梯度方法在这些情况下可以收敛到最优解，并部分支持最近在许多MARL测试床上表现良好的PG方法。得出实验结果后，我们提出实用建议，并在简化的矩阵和网格世界游戏以及StarCraft多代理挑战和谷歌研究足球等各种领域进行了实证验证。希望我们的研究能够为开发更普遍和更强大的MARL算法的社区带来益处。

合作多智能体强化学习中常见实践的重新审视