Emergent Complexity via Multi-Agent Competition
  Reinforcement learning algorithms can train agents that solve problems in
complex, interesting environments. Normally, the complexity of the trained
agent is closely related to the complexity of the environment. This suggests
that a highly capable agent requires a complex environment for training. In
this paper, we point out that a competitive multi-agent environment trained
with self-play can produce behaviors that are far more complex than the
environment itself. We also point out that such environments come with a
natural curriculum, because for any skill level, an environment full of agents
of this level will have the right level of difficulty. This work introduces
several competitive multi-agent environments where agents compete in a 3D world
with simulated physics. The trained agents learn a wide variety of complex and
interesting skills, even though the environment themselves are relatively
simple. The skills include behaviors such as running, blocking, ducking,
tackling, fooling opponents, kicking, and defending using both arms and legs. A
highlight of the learned behaviors can be found here: https://goo.gl/eR7fbX
