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.
翻译:在MOBA游戏中,英雄的起草工作至关重要,因为它可以建立双方的团队,直接影响到匹配的结果。最先进的起草方法没有考虑:(1) 在英雄池扩大时起草效率;(2) MBA 5v5比赛系列的多轮性质,即两个队发挥最佳N型和同一个英雄的作用,在整个系列中只允许一次起草。在本文件中,我们把起草过程设计成一个多轮组合游戏,并提议一种基于神经网络和蒙特-卡洛树搜索的新起草算法,名为JueWu Graft。具体地说,我们设计了一个长期的价值估算机制来处理最佳N型起草案。以国王为荣耀,这是目前最受欢迎的MOBA游戏之一,作为一个运行案例,我们展示了JueWu草案与最先进的起草方法相比的实际性和有效性。