Which Heroes to Pick? Learning to Draft in MOBA Games with Neural
  Networks and Tree Search
  Hero drafting is essential in MOBA game playing as it builds the team of each
side and directly affects the match outcome. State-of-the-art drafting methods
fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the
multi-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-N
and the same hero is only allowed to be drafted once throughout the series. In
this paper, we formulate the drafting process as a multi-round combinatorial
game and propose a novel drafting algorithm based on neural networks and
Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term
value estimation mechanism to handle the best-of-N drafting case. Taking Honor
of Kings, one of the most popular MOBA games at present, as a running case, we
demonstrate the practicality and effectiveness of JueWuDraft when compared to
state-of-the-art drafting methods.
