Knowledge Commons of Institute of Automation,CAS
Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network | |
Zhu, Jiagang1,2![]() ![]() ![]() | |
2018-11 | |
会议名称 | 2018 24th International Conference on Pattern Recognition (ICPR) |
会议日期 | 20-24 Aug. 2018 |
会议地点 | Beijing, China |
摘要 | This paper presents an approach for learning the evasion strategy for the evader in pursuit-evasion against the pursuers with Deep Q-network (DQN). To give the immediate reward to the agent, we handcraft a reward function, which considers both the evader escaping from being surrounded by the pursuers and keeping distance from the pursuers. This is a combination of the artificial potential field method with deep reinforcement learning. Our learned evasion strategy is verified by a series of experiments in three different game scenarios. The training stability and the value function are analyzed respectively. The three learned agents are compared with a random agent and a repulsive agent. We show the effectiveness of our method. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39110 |
专题 | 中国科学院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Zou, Wei |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
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 | Zhu, Jiagang,Zou, Wei,Zhu, Zheng. Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network[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 | ||
Learning Evasion Str(1396KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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