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

Hero drafting is essential in multiplayer online battle arena (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 multiround nature of a MOBA 5v5 match series, i.e., two teams play best-of-
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and the same hero is only allowed to be drafted once throughout the series. In this article, we formulate the drafting process as a multiround 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-
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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.