Harfang3D Dog-Fight Sandbox：一个战斗机定制控制任务的强化学习研究平台
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter Aircrafts
摘要：
深度学习（DL）的出现为强化学习（RL）研究带来了重大突破。深度强化学习（DRL）算法在应用于Atari 2600游戏中基于视觉的控制问题时已经达到了超人水平的技能，其中环境状态是从像素信息中提取的。不幸的是，这些环境是远远不适用于高度动态和复杂的现实世界的任务，如在战斗机的自主控制，因为这些环境只涉及一个视觉世界的2D表示。在这里，我们提出了一个半真实感的战斗机飞行仿真环境Harfang 3D Dog-Fight沙箱。它旨在成为一个灵活的工具箱，用于使用强化学习调查航空研究中的主要挑战。该程序提供了对飞行动力学模型，环境状态和飞机空气动力学的轻松访问，使用户能够定制任何特定任务，以便通过RL构建智能决策（控制）系统。该软件还允许部署机器人飞机和开发多智能体任务。通过这种方式，多组飞机可以被配置为竞争或合作代理，以执行包括Dog Fight在内的复杂任务。在实验过程中，我们进行了两种不同场景的训练：导航到指定位置和视觉范围内（WVR）的战斗，简称斗狗。在这两种情况下使用深度强化学习技术，我们能够训练表现出类似人类行为的称职代理。在此基础上，证实了Harfang 3D Dog-Fight Sandbox可以用作3D真实感RL研究平台。
英文摘要：
The advent of deep learning (DL) gave rise to significant breakthroughs in Reinforcement Learning (RL) research. Deep Reinforcement Learning (DRL) algorithms have reached super-human level skills when applied to vision-based control problems as such in Atari 2600 games where environment states were extracted from pixel information. Unfortunately, these environments are far from being applicable to highly dynamic and complex real-world tasks as in autonomous control of a fighter aircraft since these environments only involve 2D representation of a visual world. Here, we present a semi-realistic flight simulation environment Harfang3D Dog-Fight Sandbox for fighter aircrafts. It is aimed to be a flexible toolbox for the investigation of main challenges in aviation studies using Reinforcement Learning. The program provides easy access to flight dynamics model, environment states, and aerodynamics of the plane enabling user to customize any specific task in order to build intelligent decision making (control) systems via RL. The software also allows deployment of bot aircrafts and development of multi-agent tasks. This way, multiple groups of aircrafts can be configured to be competitive or cooperative agents to perform complicated tasks including Dog Fight. During the experiments, we carried out training for two different scenarios: navigating to a designated location and within visual range (WVR) combat, shortly Dog Fight. Using Deep Reinforcement Learning techniques for both scenarios, we were able to train competent agents that exhibit human-like behaviours. Based on this results, it is confirmed that Harfang3D Dog-Fight Sandbox can be utilized as a 3D realistic RL research platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
