Multi-UAV Cooperative Short-Range Combat via Attention-Based Reinforcement Learning using Individual Reward Shaping | |
Zhang TL(张天乐)1,2; Qiu TH(丘腾海)1; Liu Z(刘振)1,2; Pu ZQ(蒲志强)1,2; Yi JQ(易建强)1,2 | |
2022 | |
会议日期 | October 23-27, 2022 |
会议地点 | Kyoto, Japan |
英文摘要 | 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. |
会议录出版者 | IEEE |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51960] |
专题 | 综合信息系统研究中心_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 |
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]. 见:. Kyoto, Japan. October 23-27, 2022. |
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