  Most recently developed approaches to cooperative multi-agent reinforcement
learning in the \emph{centralized training with decentralized execution}
setting involve estimating a centralized, joint value function. In this paper,
we demonstrate that, despite its various theoretical shortcomings, Independent
PPO (IPPO), a form of independent learning in which each agent simply estimates
its local value function, can perform just as well as or better than
state-of-the-art joint learning approaches on popular multi-agent benchmark
suite SMAC with little hyperparameter tuning. We also compare IPPO to several
variants; the results suggest that IPPO's strong performance may be due to its
robustness to some forms of environment non-stationarity.
