Knowledge Commons of Institute of Automation,CAS
Reinforcement Learning for Build-Order Production in StarCraft II | |
Zhentao Tang1,2; Dongbin Zhao1,2; Yuanheng Zhu1,2; Ping Guo3 | |
2018 | |
会议名称 | 2018 Eighth International Conference on Information Science and Technology (ICIST) |
会议日期 | 30 June-6 July 2018 |
会议地点 | Cordoba, Granada, and Seville, Spain |
摘要 | 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. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45049 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Dongbin Zhao |
作者单位 | 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences, Beijing, China 100190 3.School of Systems Science Beijing Normal University Beijing, China 100875 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhentao Tang,Dongbin Zhao,Yuanheng Zhu,et al. Reinforcement Learning for Build-Order Production in StarCraft II[C],2018. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Reinforcement Learni(2680KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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