Knowledge Commons of Institute of Automation,CAS
Multi-UAV Cooperative Short-Range Combat via Attention-Based Reinforcement Learning using Individual Reward Shaping | |
Zhang TL(张天乐)1,2![]() ![]() ![]() ![]() ![]() | |
2022 | |
会议名称 | IEEE/RSJ International Conference on Intelligent Robots and Systems |
会议日期 | October 23-27, 2022 |
会议地点 | Kyoto, Japan |
出版者 | IEEE |
摘要 | In this paper, we propose a novel distributed method based on attention-based deep reinforcement learning using individual reward shaping, for multiple unmanned aerial vehicles (UAVs) cooperative short-range combat mission. Specifically, a two-level attention distributed policy, composed of observation-level and communication-level attention networks, is designed to enable each UAV to selectively focus on important environmental features and messages, for enhancing the effectiveness of the cooperative policy. Moreover, due to the high complexity and stochasticity of the UAV combat mission, the learning of UAVs is tricky and low efficient. To embed knowledge to accelerate the policy learning, a potential-based individual reward function is constructed by implicitly translating the individual reward into the specific form of dynamic action potentials. In addition, an actor-critic training algorithm based on the centralized training and decentralized execution framework is adopted to train the policy network of UAV maneuver decision. We build a three-dimensional UAV simulation and training platform based on Unity for multi-UAV short-range combat missions. Simulation results demonstrate the effectiveness of the proposed method and the superiority of the attention policy and individual reward shaping. |
收录类别 | EI |
语种 | 英语 |
是否为代表性论文 | 是 |
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/173211/51960 |
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 | Qiu TH(丘腾海) |
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 |
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 | Zhang TL,Qiu TH,Liu Z,et al. Multi-UAV Cooperative Short-Range Combat via Attention-Based Reinforcement Learning using Individual Reward Shaping[C]:IEEE,2022. |
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 | ||
2022-IROS.pdf(896KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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