Summary of Bvr Gym: a Reinforcement Learning Environment For Beyond-visual-range Air Combat, by Edvards Scukins et al.
BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat
by Edvards Scukins, Markus Klein, Lars Kroon, Petter Ögren
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the BVR Gym, a reinforcement learning environment designed to investigate potential air combat tactics in beyond-visual-range (BVR) air combat. The BVR Gym addresses the limitations of existing environments by providing high-fidelity simulations based on the open-source flight dynamics simulator JSBSim, tailored specifically for the BVR air combat domain. This medium-difficulty summary highlights the paper’s key contributions and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Air combat tactics are crucial in beyond-visual-range (BVR) air combat, where long-range missiles are often used first. The BVR Gym is a new way to investigate potential tactics using reinforcement learning. It provides high-fidelity simulations based on JSBSim, which is open-source and specific to the BVR air combat domain. |
Keywords
* Artificial intelligence * Reinforcement learning
