  This work proposes a scheme that allows learning complex multi-agent
behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is
formulated as a Markov game, and solved using deep reinforcement learning. We
propose a basic multi-agent extension of TD3 for learning the policy of each
player, in a decentralized manner. To ease learning, the task of 2v2 soccer is
divided in three stages: 1v0, 1v1 and 2v2. The process of learning in
multi-agent stages (1v1 and 2v2) uses agents trained on a previous stage as
fixed opponents. In addition, we propose using experience sharing, a method
that shares experience from a fixed opponent, trained in a previous stage, for
training the agent currently learning, and a form of frame-skipping, to raise
performance significantly. Our results show that high quality soccer play can
be obtained with our approach in just under 40M interactions. A summarized
video of the resulting game play can be found in https://youtu.be/f25l1j1U9RM.
