{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T15:57:25Z","timestamp":1726329445212},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"04","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"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). We also provide a diverse set of simpler scenarios with the Football Academy and showcase several promising research directions.<\/jats:p>","DOI":"10.1609\/aaai.v34i04.5878","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T21:34:07Z","timestamp":1593466447000},"page":"4501-4510","source":"Crossref","is-referenced-by-count":103,"title":["Google Research Football: A Novel Reinforcement Learning Environment"],"prefix":"10.1609","volume":"34","author":[{"given":"Karol","family":"Kurach","sequence":"first","affiliation":[]},{"given":"Anton","family":"Raichuk","sequence":"additional","affiliation":[]},{"given":"Piotr","family":"Sta\u0144czyk","sequence":"additional","affiliation":[]},{"given":"Micha\u0142","family":"Zaj\u0105c","sequence":"additional","affiliation":[]},{"given":"Olivier","family":"Bachem","sequence":"additional","affiliation":[]},{"given":"Lasse","family":"Espeholt","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Riquelme","sequence":"additional","affiliation":[]},{"given":"Damien","family":"Vincent","sequence":"additional","affiliation":[]},{"given":"Marcin","family":"Michalski","sequence":"additional","affiliation":[]},{"given":"Olivier","family":"Bousquet","sequence":"additional","affiliation":[]},{"given":"Sylvain","family":"Gelly","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2020,4,3]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/5878\/5734","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/5878\/5734","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:46Z","timestamp":1667520046000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2020,6,16]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v34i04.5878","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4,3]]}}}