Our agents, with 20 actuated joints, were trained in simulation using the MuJoCo physics engine, and transferred zero-shot to real robots. The agents use proprioception and game state features as observations. The trained soccer players exhibit robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more. They transition between these emergent skills automatically in a smooth, stable, and efficient manner, going beyond what might intuitively be expected from the platform. The agents also developed a basic strategic understanding of the game, learning to anticipate ball movements and to block opponent shots.