Tunnel让强化学习训练飞机模拟更高效，支持快速适配作战环境。
Training Environment for High Performance Reinforcement Learning
- 基于F16非线性飞行模型，集成到Gymnasium环境
- 一周内完成多种训练方法与威胁场景的对比实验
- 适合研究人员和任务规划者快速迭代自主空战策略
本文提出Tunnel，一个简单、开源的强化学习训练环境，用于高性能飞机。它将F16三维非线性飞行动力学整合至OpenAI Gymnasium Python框架中。环境模板包含边界、目标、敌方及感知能力等可配置组件，可根据任务需求动态调整。该平台使任务规划者能快速响应不断变化的作战环境、传感器能力和对手威胁，为自主空战飞机提供支持。研究人员可获取具有实战意义的飞机物理模型。Tunnel代码对熟悉Gymnasium或具备基础Python技能者开放。论文展示了为期一周的权衡研究，涵盖多种训练方法、观测空间和威胁呈现方式。这促进了研究者与任务规划者间的协作，有望转化为国家军事优势。随着战争日益依赖自动化，软件敏捷性将带来决策优势。空军人员需具备适应对手变化的工具。传统空战模拟器中定制观测、动作、任务和训练方法通常需数月，而在Tunnel中仅需数天即可完成。
原文摘要 · Abstract (English)
This paper presents Tunnel, a simple, open source, reinforcement learning training environment for high performance aircraft. It integrates the F16 3D nonlinear flight dynamics into OpenAI Gymnasium python package. The template includes primitives for boundaries, targets, adversaries and sensing capabilities that may vary depending on operational need. This offers mission planners a means to rapidly respond to evolving environments, sensor capabilities and adversaries for autonomous air combat aircraft. It offers researchers access to operationally relevant aircraft physics. Tunnel code base is accessible to anyone familiar with Gymnasium and/or those with basic python skills. This paper includes a demonstration of a week long trade study that investigated a variety of training methods, observation spaces, and threat presentations. This enables increased collaboration between researchers and mission planners which can translate to a national military advantage. As warfare becomes increasingly reliant upon automation, software agility will correlate with decision advantages. Airmen must have tools to adapt to adversaries in this context. It may take months for researchers to develop skills to customize observation, actions, tasks and training methodologies in air combat simulators. In Tunnel, this can be done in a matter of days.
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