论文标题
AAM-GYM：高级空气移动性的人工智能测试
AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility
论文作者
论文摘要
我们介绍了AAM-GYM，这是一种用于高级空气流动性（AAM）的研发测试床。 AAM有可能通过利用新型的飞机（例如电动垂直起飞和降落（EVTOL）飞机）和新的高级人工智能（AI）算法来减少地面交通和排放来彻底改变旅行。 AI算法的验证需要代表性的AAM场景，以及快速的仿真测试以评估其性能。到目前为止，AAM还没有这样的测试床可以为政府，工业或学术界的个人提供一个共同的研究平台。麻省理工学院林肯实验室（MIT Lincoln Laboratory）通过提供一个生态系统来开发，训练和验证新的AI算法，以解决这一差距，以解决这一差距。在本文中，我们使用AAM-GYM来研究AAM用例，AAM走廊中的分离保证的两种增强学习算法的性能。根据AAM-GYM提供的一系列指标，证明了两种算法的性能，显示了测试台对AAM研究的实用性。
We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of AI algorithms require representative AAM scenarios, as well as a fast time simulation testbed to evaluate their performance. Until now, there has been no such testbed available for AAM to enable a common research platform for individuals in government, industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address this gap by providing an ecosystem to develop, train, and validate new and established AI algorithms across a wide variety of AAM use-cases. In this paper, we use AAM-Gym to study the performance of two reinforcement learning algorithms on an AAM use-case, separation assurance in AAM corridors. The performance of the two algorithms is demonstrated based on a series of metrics provided by AAM-Gym, showing the testbed's utility to AAM research.
