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Reinforcement Learning for Build-Order Production in StarCraft II

机译：Starcraft II中的加强学习中的建立订单生产

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StarCraft II is one of the most popular real-time strategy games and has become an important benchmark for AI research as it provides a complex environment with numerous challenges. The build order problem is one of the key challenges which concern the order and type of buildings and units to produce based on current game situation. In contrast to existing hand-craft methods, we propose two reinforcement learning based models: Neural Network Fitted Q-Learning (NNFQ) and Convolutional Neural Network Fitted Q-Learning (CNNFQ). NNFQ and CNNFQ have been applied into a simple bot for fighting against the enemy race. Experimental results show that both these two models are capable of finding the most effective production sequence to defeat the opponent.
机译：星际争霸II是最受欢迎的实时战略游戏之一，已成为AI研究的重要基准，因为它提供了一个具有许多挑战的复杂环境。构建订单问题是关注基于当前游戏情况的建筑物和单位的订单和类型的关键挑战之一。与现有的手工艺方法相比，我们提出了两种基于加强学习的模型：神经网络拟合Q学习（NNFQ）和卷积神经网络拟合Q-Learning（CNNFQ）。 NNFQ和CNNFQ已被应用于一个简单的机器人，以反对敌人的比赛。实验结果表明，这两种型号都能够找到最有效的生产序列来击败对手。

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