  Proximal Policy Optimization (PPO) is a popular on-policy reinforcement
learning algorithm but is significantly less utilized than off-policy learning
algorithms in multi-agent problems. In this work, we investigate Multi-Agent
PPO (MAPPO), a multi-agent PPO variant which adopts a centralized value
function. Using a 1-GPU desktop, we show that MAPPO achieves performance
comparable to the state-of-the-art in three popular multi-agent testbeds: the
Particle World environments, Starcraft II Micromanagement Tasks, and the Hanabi
Challenge, with minimal hyperparameter tuning and without any domain-specific
algorithmic modifications or architectures. In the majority of environments, we
find that compared to off-policy baselines, MAPPO achieves better or comparable
sample complexity as well as substantially faster running time. Finally, we
present 5 factors most influential to MAPPO's practical performance with
ablation studies.
