If I read correctly, the agent only controls one player at a time. On offense it controls the player with the ball, and on defense it controls probably the player closest to the ball. The other players are controlled by the built-in AI. Controlling a single agent kind of takes away from the appeal of deep-RL: that entire teams can learn to coordinate in novel and optimal ways.
Modeled after popular football video games, the Football Environment provides a physics based 3D football simulation where agents control either one or all football players on their team, learn how to pass between them, and manage to overcome their opponent’s defense in order to score goals.
It looks like the AI agent can control one or all of the players on their team.
by default, our non-active players are also controlled by another rule-based bot. In this case, the behavior is simple and corresponds to reasonable football actions
and strategies, such as running towards the ball when we are
not in possession, or move forward together with our active
player. In particular, this type of behavior can be turned off for future research on cooperative multi-agents if desired.
Comments
If I read correctly, the agent only controls one player at a time. On offense it controls the player with the ball, and on defense it controls probably the player closest to the ball. The other players are controlled by the built-in AI. Controlling a single agent kind of takes away from the appeal of deep-RL: that entire teams can learn to coordinate in novel and optimal ways.
It looks like the AI agent can control one or all of the players on their team.
You are right! From the paper: