Knowledge Commons of Institute of Automation,CAS
Semantic Perception Swarm Policy with Deep Reinforcement Learning | |
Zhang TL(张天乐); Liu Z(刘振); Pu ZQ(蒲志强); Yi JQ(易建强) | |
2021 | |
会议名称 | International Conference on Neural Information Processing |
会议日期 | 05 December 2021 |
会议地点 | Online |
出版者 | Spring Link |
摘要 | Swarm systems with simple, homogeneous and autonomous individuals can efficiently accomplish specified complex tasks. Recent works have shown the power of deep reinforcement learning (DRL) methods to learn cooperative policies for swarm systems. However, most of them show poor adaptability when applied to new environments or tasks. In this paper, we propose a novel semantic perception swarm policy with DRL for distributed swarm systems. This policy implements innovative semantic perception, which enables agents to under- stand their observation information, yielding semantic information, to promote agents’ adaptability. In particular, semantic disentangled representation with posterior distribution and semantic mixture representation with network mapping are realized to represent semantic information of agents’ observations. Moreover, in the semantic representation, heterogeneous graph attention network is adopted to effectively model individual-level and group-level relational information. The distributed and transferable swarm policy can perceive the information of uncertain number of agents in swarm environments. Various simulations and real-world experiments on several challenging tasks, i.e., sheep food collection and wolves predation, demonstrate the superior effectiveness and adaptability performance of our method compared with existing methods. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 决策智能理论与方法 |
国重实验室规划方向分类 | 多智能体决策 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51963 |
专题 | 复杂系统认知与决策实验室_飞行器智能技术 |
通讯作者 | Liu Z(刘振) |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学人工智能学院 |
推荐引用方式 GB/T 7714 | Zhang TL,Liu Z,Pu ZQ,et al. Semantic Perception Swarm Policy with Deep Reinforcement Learning[C]:Spring Link,2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2021-ICONIP.pdf(523KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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