  We apply multi-agent deep reinforcement learning (RL) to train end-to-end
robot soccer policies with fully onboard computation and sensing via egocentric
RGB vision. This setting reflects many challenges of real-world robotics,
including active perception, agile full-body control, and long-horizon planning
in a dynamic, partially-observable, multi-agent domain. We rely on large-scale,
simulation-based data generation to obtain complex behaviors from egocentric
vision which can be successfully transferred to physical robots using low-cost
sensors. To achieve adequate visual realism, our simulation combines rigid-body
physics with learned, realistic rendering via multiple Neural Radiance Fields
(NeRFs). We combine teacher-based multi-agent RL and cross-experiment data
reuse to enable the discovery of sophisticated soccer strategies. We analyze
active-perception behaviors including object tracking and ball seeking that
emerge when simply optimizing perception-agnostic soccer play. The agents
display equivalent levels of performance and agility as policies with access to
privileged, ground-truth state. To our knowledge, this paper constitutes a
first demonstration of end-to-end training for multi-agent robot soccer,
mapping raw pixel observations to joint-level actions, that can be deployed in
the real world. Videos of the game-play and analyses can be seen on our website
https://sites.google.com/view/vision-soccer .
