{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/google-research-football-a-novel","title":"Google Research Football: A Novel Reinforcement Learning Environment","arxiv_id":"1907.11180","date":"2019-07-25","proceeding":null,"authors":["Karol Kurach","Anton Raichuk","Piotr Stańczyk","Michał Zając","Olivier Bachem","Lasse Espeholt","Carlos Riquelme","Damien Vincent","Marcin Michalski","Olivier Bousquet","Sylvain Gelly"],"abstract":"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. In addition, it provides support for multiplayer and multi-agent experiments. We propose three full-game scenarios of varying difficulty with the Football Benchmarks and report baseline results for three commonly used reinforcement algorithms (IMPALA, PPO, and Ape-X DQN). 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