Learning Evasion Strategy in Pursuit-Evasion by Deep Q-network | |
Zhu, Jiagang1,2; Zou, Wei1; Zhu, Zheng1,2 | |
2018-11 | |
会议日期 | 20-24 Aug. 2018 |
会议地点 | Beijing, China |
英文摘要 | 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. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39110] |
专题 | 精密感知与控制研究中心_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 |
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]. 见:. Beijing, China. 20-24 Aug. 2018. |
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