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Hero drafting is essential in MOBA game playing as it builds the team of eachside and directly affects the match outcome. State-of-the-art drafting methodsfail to consider: 1) drafting efficiency when the hero pool is expanded; 2) themulti-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-Nand the same hero is only allowed to be drafted once throughout the series. Inthis paper, we formulate the drafting process as a multi-round combinatorialgame and propose a novel drafting algorithm based on neural networks andMonte-Carlo tree search, named JueWuDraft. Specifically, we design a long-termvalue estimation mechanism to handle the best-of-N drafting case. Taking Honorof Kings, one of the most popular MOBA games at present, as a running case, wedemonstrate the practicality and effectiveness of JueWuDraft when compared tostate-of-the-art drafting methods.

Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search