StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning
	Kun Shao1,2 ; Yuanheng Zhu1,2 ; Dongbin Zhao1,2
发表期刊	IEEE Transactions on Emerging Topics in Computational Intelligence
ISSN	2471-285X
	2019-02
卷号	3 期号:1 页码:73-84
摘要	Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then, a parameter sharing multi-agent gradientdescent Sarsa(λ) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action–value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small-scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large-scale scenarios, the curriculum transfer learning method is used to progressively train a group of units, and it shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
关键词	Reinforcement Learning, Transfer Learning, Curriculum Learning, Neural Network, Game Ai
学科门类	工学
收录类别	EI
语种	英语
七大方向——子方向分类	强化与进化学习
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/23362
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.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences
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	Kun Shao,Yuanheng Zhu,Dongbin Zhao. StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning[J]. IEEE Transactions on Emerging Topics in Computational Intelligence,2019,3(1):73-84.
APA	Kun Shao,Yuanheng Zhu,&Dongbin Zhao.(2019).StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning.IEEE Transactions on Emerging Topics in Computational Intelligence,3(1),73-84.
MLA	Kun Shao,et al."StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning".IEEE Transactions on Emerging Topics in Computational Intelligence 3.1(2019):73-84.

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