论文标题
SCC：有效的深入强化学习代理人掌握Starcraft II的游戏
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
论文作者
论文摘要
Alphastar是Starcraft II中达到大师级级别的AI，是一个了不起的里程碑，展示了深厚的实时策略（RTS）游戏可以实现的深入强化。但是，游戏，算法和系统的复杂性，尤其是所需的大量计算是社区朝这个方向进行进一步研究的巨大障碍。我们提出了一位深入的增强学习代理，星际争霸指挥官（SCC）。随着计算的数量级，它表明了在测试比赛中击败大师级球员的最佳人力表现，并且在现场比赛中表现出了顶级专业球员。此外，它对各种人类策略显示出强大的鲁棒性，并发现了从人类戏剧中看不见的新型策略。在本文中，我们将分享有关Starcraft II完整游戏的有效模仿学习和强化学习的关键见解和优化。
AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.
