TStarBot-X：一项开放性的、综合性的研究，旨在提高StarCraft II全运会联赛训练的效率
TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game
摘要：
《星际争霸》是职业赛事中难度最高的电子竞技游戏之一，有着悠久的历史，吸引了一代又一代的玩家和粉丝，也引起了人工智能研究的强烈关注。最近，谷歌的DeepMind宣布了AlphaStar，这是星际争霸II中的一个大师级AI，可以使用类似的动作空间和操作与人类一起玩。在本文中，我们介绍了一种新的AI代理，名为TStarBot-X，它是在计算量较少的情况下训练的，可以与专家玩家进行竞争。TStarBot-X利用了AlphaStar中引入的重要技术，并受益于实质性的创新，包括新的联盟训练方法，新颖的多智能体角色，规则引导的策略搜索，稳定的策略改进，轻量级神经网络架构，以及模仿学习中的重要性采样等。AlphaStar方法的忠实重新实现不能成功，并且所提出的技术对于确保TStarBot-X的竞争性能是必要的。我们揭示了与AlphaStar中提到的技术细节互补的所有技术细节，显示了联盟训练中最敏感的部分，强化学习和模仿学习会影响代理的性能。最重要的是，这是一项开源研究，所有代码和资源（包括训练的模型参数）都可以通过\url{https://github.com/tencent-ailab/tleague_projpage}公开访问。我们希望这项研究可以为学术和工业界解决像星际争霸这样的复杂问题的未来研究带来益处，也可以为所有星际争霸II玩家和其他人工智能智能体提供一个陪练伙伴。
英文摘要：
StarCraft, one of the most difficult esport games with long-standing history of professional tournaments, has attracted generations of players and fans, and also, intense attentions in artificial intelligence research. Recently, Google's DeepMind announced AlphaStar, a grandmaster level AI in StarCraft II that can play with humans using comparable action space and operations. In this paper, we introduce a new AI agent, named TStarBot-X, that is trained under orders of less computations and can play competitively with expert human players. TStarBot-X takes advantage of important techniques introduced in AlphaStar, and also benefits from substantial innovations including new league training methods, novel multi-agent roles, rule-guided policy search, stabilized policy improvement, lightweight neural network architecture, and importance sampling in imitation learning, etc. We show that with orders of less computation scale, a faithful reimplementation of AlphaStar's methods can not succeed and the proposed techniques are necessary to ensure TStarBot-X's competitive performance. We reveal all technical details that are complementary to those mentioned in AlphaStar, showing the most sensitive parts in league training, reinforcement learning and imitation learning that affect the performance of the agents. Most importantly, this is an open-sourced study that all codes and resources (including the trained model parameters) are publicly accessible via \url{https://github.com/tencent-ailab/tleague_projpage}. We expect this study could be beneficial for both academic and industrial future research in solving complex problems like StarCraft, and also, might provide a sparring partner for all StarCraft II players and other AI agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
