About a dozen members of the Google Brain team today open-sourced Google Research Football Environment, a 3D reinforcement learning simulator for training AI to master soccer. The environment can simulate soccer matches, including particular scenarios like corner and penalty kicks, goals, and offsides. The news comes today at the start of the Women’s World Cup starts in France and a day after Google introduced pricing and games for its Stadia cloud gaming service.
“Researchers can directly experience how the game works by playing against each other or by dueling their agents. The game can be controlled by means of both keyboards and gamepads. Moreover, replays of several rendering qualities can be automatically stored while training, so that it is easy to inspect the policies agents are learning,” researchers said in a paper that accompanies the package on GitHub.
The environment also includes state-of-the-art reinforcement learning algorithms proximal policy optimization (PPO), DQN, and Impala, as well as a set of about a dozen different scenarios for training AI agents in what the researchers call the Football Academy.
This practice environment for particular scenarios includes corner kicks, 3-on-1 matches, and 11-on-11 matches with lazy opponents. In initial results detailed in the research paper, Impala trained on 500 million steps saw the best performance.
The 3D simulator can take into account both the location of a player on the pitch and raw pixel analysis to find the best way to pass the ball, overcome obstacles, defend forwards, and score goals.
Reinforcement learning through simulations has been applied to accomplish a number of challenging gaming tasks like training agents to beat humans in Starcraft, Quake III, Go, and Pong, but it’s also being used for a range of jobs from robotic arm and leg control to online recommendation tools.
The Google Research Football Environment works with OpenAI’s Gym reinforcement learning environment.