Summary of Autonomous Decision Making For Uav Cooperative Pursuit-evasion Game with Reinforcement Learning, by Yang Zhao et al.
Autonomous Decision Making for UAV Cooperative Pursuit-Evasion Game with Reinforcement Learning
by Yang Zhao, Zidong Nie, Kangsheng Dong, Qinghua Huang, Xuelong Li
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep reinforcement learning-based model for decision-making in multi-role UAV cooperative pursuit-evasion games uses a multi-environment asynchronous double deep Q-network with priority experience replay algorithm to train the UAV’s game policy efficiently. The model enables autonomous decision-making of the UAVs and exhibits significant capabilities in cooperation, addressing the challenge of enabling UAVs to make decisions in complex game environments. By assigning diverse tasks and roles to the UAVs in different scenarios, the proposed method obtains cooperative game decision models with varying numbers of UAVs. This approach can be applied in various pursuit-evasion game scenarios, enhancing training efficiency and minimizing the cost of UAVs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new way for drones to work together autonomously using deep learning and reinforcement learning. They created a model that allows drones to make decisions in complex situations, like pursuing or evading each other. The model uses a special type of artificial intelligence called double deep Q-networks, which helps the drones learn faster and make better decisions. This technology can be used in many different scenarios where drones need to work together, making it more efficient and cost-effective. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
