论文标题
哪个英雄要选？学习通过神经网络和树木搜索在MOBA游戏中起草
Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search
论文作者
论文摘要
英雄制图在MOBA游戏中至关重要，因为它可以建立双方的团队并直接影响比赛结果。最新的起草方法无法考虑：1）在扩大英雄池时起草效率； 2）MOBA 5V5比赛系列的多轮本质，即两支球队的比赛最佳和同一英雄只能在整个系列赛中起草一次。在本文中，我们将起草过程作为多轮组合游戏制定，并提出了一种基于神经网络和蒙特卡洛树搜索的新颖起草算法，名为Juewudraft。具体而言，我们设计了一种长期价值估计机制来处理最佳N起草情况。作为目前最受欢迎的MOBA游戏之一Kings纪念Kings，作为一个跑步案例，我们证明了Juewudraft的实用性和有效性，与最先进的起草方法相比。
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.
