论文标题
Starcraft II使用深厚的增强学习和蒙特卡洛树搜索建立订单优化
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search
论文作者
论文摘要
Google的DeepMind构成了Starcraft II的实时策略游戏作为增强学习的挑战。这项研究研究了基于蒙特卡洛树搜索算法的代理使用代理，以优化星际争霸II中的构建顺序，并通过将其与深入的增强学习神经网络相结合，讨论如何进一步提高其性能。使用蒙特卡洛树搜索实现的实验结果仅通过使用非常有限的时间和计算资源来达到与新手人类玩家相似的分数，这为实现与人类专家的分数相当，通过将其与深入的增强学习结合结合来实现与人类专家的分数相似。
The real-time strategy game of StarCraft II has been posed as a challenge for reinforcement learning by Google's DeepMind. This study examines the use of an agent based on the Monte-Carlo Tree Search algorithm for optimizing the build order in StarCraft II, and discusses how its performance can be improved even further by combining it with a deep reinforcement learning neural network. The experimental results accomplished using Monte-Carlo Tree Search achieves a score similar to a novice human player by only using very limited time and computational resources, which paves the way to achieving scores comparable to those of a human expert by combining it with the use of deep reinforcement learning.
