  MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand
challenges to AI systems such as multi-agent, enormous state-action space,
complex action control, etc. Developing AI for playing MOBA games has raised
much attention accordingly. However, existing work falls short in handling the
raw game complexity caused by the explosion of agent combinations, i.e.,
lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the
play to a pool of only 17 heroes. As a result, full MOBA games without
restrictions are far from being mastered by any existing AI system. In this
paper, we propose a MOBA AI learning paradigm that methodologically enables
playing full MOBA games with deep reinforcement learning. Specifically, we
develop a combination of novel and existing learning techniques, including
curriculum self-play learning, policy distillation, off-policy adaption,
multi-head value estimation, and Monte-Carlo tree-search, in training and
playing a large pool of heroes, meanwhile addressing the scalability issue
skillfully. Tested on Honor of Kings, a popular MOBA game, we show how to build
superhuman AI agents that can defeat top esports players. The superiority of
our AI is demonstrated by the first large-scale performance test of MOBA AI
agent in the literature.
