Abstract
Reinforcement learning (RL) demonstrates superior potential over traditional flight control methods for fixed-wing aircraft, particularly under extreme operational conditions. However, the high demand for training samples and the lack of efficient computation in existing simulators hinder its further application. In this paper, we introduce NeuralPlane, the first benchmark platform for large-scale parallel simulations of fixed-wing aircraft. NeuralPlane significantly boosts high-fidelity simulation via GPU-accelerated Flight Dynamics Model (FDM) computation, achieving a single-step simulation time of just 0.2 seconds at a parallel scale of 106 aircraft, far exceeding current platforms. We also provide clear code templates, comprehensive evaluation and visualization tools, and hierarchical frameworks for integrating RL and traditional control methods. We believe that NeuralPlane can accelerate the development of RL-based fixed-wing flight control and serve as a new challenging benchmark for the RL community. Our NeuralPlane is open-source and accessible at https://github.com/xuecy22/NeuralPlane.
Original language | English |
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Journal | Advances in Neural Information Processing Systems |
Volume | 37 |
Publication status | Published - 2024 |
Externally published | Yes |
Event | 38th Conference on Neural Information Processing Systems, NeurIPS 2024 - Vancouver, Canada Duration: Dec 9 2024 → Dec 15 2024 |
Bibliographical note
Publisher Copyright:© 2024 Neural information processing systems foundation. All rights reserved.
ASJC Scopus Subject Areas
- Computer Networks and Communications
- Information Systems
- Signal Processing