NeuralPlane: An Efficiently Parallelizable Platform for Fixed-wing Aircraft Control with Reinforcement Learning

Chuanyi Xue, Qihan Liu, Xiaoteng Ma, Yang Qi, Xinyao Qin, Yuhua Jiang, Ning Gui, Jinsheng Ren, Bin Liang, Jun Yang*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish
JournalAdvances in Neural Information Processing Systems
Volume37
Publication statusPublished - 2024
Externally publishedYes
Event38th Conference on Neural Information Processing Systems, NeurIPS 2024 - Vancouver, Canada
Duration: Dec 9 2024Dec 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

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