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

• URL: http://arxiv.org/abs/2012.10171v1
• Date: Fri, 18 Dec 2020 11:19:00 GMT
• Title: Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search
• Authors: Sheng Chen, Menghui Zhu, Deheng Ye, Weinan Zhang, Qiang Fu, Wei Yang
• Abstract summary: State-of-the-art drafting methods fail to consider the multi-round nature of a MOBA 5v5 match series. We propose a novel drafting algorithm based on neural networks and Monte-Carlo tree search, named JueWuDraft. We demonstrate the practicality and effectiveness of JueWuDraft when compared to state-of-the-art drafting methods.
• Score: 33.23242783135013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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