  Asymmetrical multiplayer (AMP) game is a popular game genre which involves
multiple types of agents competing or collaborating with each other in the
game. It is difficult to train powerful agents that can defeat top human
players in AMP games by typical self-play training method because of
unbalancing characteristics in their asymmetrical environments. We propose
asymmetric-evolution training (AET), a novel multi-agent reinforcement learning
framework that can train multiple kinds of agents simultaneously in AMP game.
We designed adaptive data adjustment (ADA) and environment randomization (ER)
to optimize the AET process. We tested our method in a complex AMP game named
Tom \& Jerry, and our AIs trained without using any human data can achieve a
win rate of 98.5% against top human players over 65 matches. The ablation
experiments indicated that the proposed modules are beneficial to the
framework.
