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Google Research Football: A Novel Reinforcement Learning Environment

Karol Kurach
Piotr Michal Stanczyk
Michał Zając
Carlos Riquelme
Damien Vincent
Marcin Michalski
Sylvain Gelly
AAAI (2019)


Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Research Football Environment, a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. The resulting environment is challenging, easy to use and customize, and it is available under a permissive open-source license. We further propose three full-game scenarios of varying difficulty with the Football Benchmarks, we report baseline results for three commonly used reinforcement algorithms (Impala, PPO, and Ape-X DQN), and we also provide a diverse set of simpler scenarios with the Football Academy.

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