Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search
This work tackles the complex hero drafting problem for MOBA game players and AI, especially for multi-round series, which is an incremental improvement over existing methods.
This paper addresses the hero drafting problem in MOBA games, particularly focusing on multi-round best-of-N match series where heroes cannot be re-drafted. The proposed JueWuDraft algorithm, combining neural networks and Monte-Carlo tree search, demonstrates practical effectiveness against state-of-the-art methods in Honor of Kings.
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.