A Deep Reinforcement Learning Enabled Large-scale UAV Network with Distributed Navigation, Mobility Control, and Resilience

Organization: Ohio State University
Award ID: 2148253
PI: Yingbin Liang
Co-PI(s): Junshan Zhang, Hai Li, Qinru Qiu
Contact: liang.889@osu.edu
Thanks to the significant technological advances in unmanned aerial vehicles (UAVs), the past few years have witnessed an explosive growth of UAVs in civilian and commercial applications. To ensure efficient and reliable auto-navigation and planning in these applications, there is an urgent need to develop the foundational technology that enables UAVs to have strong sensing, communications, and on-board computing capabilities, to adapt rapidly to the dynamically changing environment, to be resilient to extreme environmental conditions, and to be secure against data contamination and malicious attacks. The interdisciplinary nature of the proposed research will provide valuable research opportunities and hands-on projects for a diverse group of students.

The goal of this project is to leverage and significantly advance the recent breakthroughs in NextG wireless communications, deep machine learning, hardware-aware model generation, and robust and trustworthy artificial intelligence, to enable the design of an intelligent and resilient UAV navigation and planning system. More specifically, this project will develop: (a) real-time communication assisted ambient sensing with multi-modality data fusion and machine learning assisted fast processing for global state tracking; (b) a multi-agent decentralized reinforcement learning (RL) framework with highly scalable computations and flexible latency tolerance; (c) deep learning based message passing for efficient communication and powerful hardware-aware neural architecture search for efficient on-board computation; and (d) comprehensive robustness and security design for system protection from outlier data, malicious poisoning attacks, and RL system attacks. The project will also conduct extensive performance evaluations to validate the developed approaches and algorithms.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Publications and News

  • Zhang, Tunhou and Ma, Mingyuan and Yan, Feng and Li, Hai and Chen, Yiran.. “PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), v, 2023. – Citation Details
  • Hang Wang and Sen Lin and Junshan Zhang. “Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap” 2023 International Conference on Machine Learning, v, 2023. – Citation Details
  • Shi, M. and Liang, Y. and Shroff, N.. “Near-optimal adversarial reinforcement learning with switching costs” International Conference on Learning Representations (ICLR), v, 2023. – Citation Details
  • Huang, R. and Yang, J. and Liang Y.. “Safe Exploration incurs nearly no additional sample complexity for reward-free RL” International Conference on Learning Representations (ICLR), v, 2023. – Citation Details
  • Xu, Tengyu and Yang, Zhuoran and Wang, Zhaoran and Liang, Yingbin.. “A unified off-policy evaluation approach for general value function” Advances in Neural Information Processing Systems (NeurIPS), v, 2022. – Citation Details
  • Feng, Songtao and Yin, Ming and Huang, Ruiquan and Wang, Yu-Xiang and Yang, Jing and Liang, Yingbin.. “Non-stationary reinforcement learning under general function approximation” Proc. International Conference on Machine Learning (ICML), v, 2023. – Citation Details
  • Yue, S. and Wang, G. and Shao, W. and Zhang, Z. and Lin, S. and Ren, J. and Zhang, J.. “CLARE: Conservative Model-based Reward Learning for Offline Inverse Reinforcement Learning” International Conference on Learning Representations (ICLR), v, 2023. – Citation Details