Summary of An Imitative Reinforcement Learning Framework For Autonomous Dogfight, by Siyuan Li et al.
An Imitative Reinforcement Learning Framework for Autonomous Dogfight
by Siyuan Li, Rongchang Zuo, Bofei Liu, Peng Liu, Yingnan Zhao
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 novel imitative reinforcement learning framework efficiently leverages expert data while enabling autonomous exploration for developing successful dogfight policies of ‘pursuit-lock-launch’ for Unmanned Combat Aerial Vehicles (UCAVs). The framework combines the benefits of imitation and reinforcement learning to overcome challenges such as weak exploration capabilities, low learning efficiency, and unrealistic simulated environments. The framework is evaluated in a Harfang3D sandbox environment and achieves a 100% success rate with excellent robustness. This paper demonstrates significant performance improvements over state-of-the-art methods in multistage dogfight tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create autonomous UCAVs that can fight each other more effectively. It does this by developing a new way to learn from experts and try new things on its own. The current challenge is that existing methods don’t work well in realistic situations, so the new framework tries to address these issues. The results show that it performs much better than previous methods in complex dogfight scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
