The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games

• URL: http://arxiv.org/abs/2103.01955v1
• Date: Tue, 2 Mar 2021 18:59:56 GMT
• Title: The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games
• Authors: Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu
• Abstract summary: Multi-Agent PPO (MAPPO) is a multi-agent PPO variant which adopts a centralized value function. We show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds.
• Score: 67.47961797770249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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