publications

2026

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    LLM-Assisted Multi-Agent Reinforcement Learning for Low-Carbon Intelligent Transportation Systems
    S. Dai , X. Wu , J. Nian , C. Liu , F. Fang , K. Makantasis , G. Theodoropoulos , and L. Cheng
    IEEE Transactions on Intelligent Transportation Systems, 2026
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    Hybrid Quantum-Classical Reinforcement Learning for Scheduling Systems: Hybrid Quantum-Classical Machine Learning Systems
    Y. Gu , S. Dai , C. Liu , Q. Wang , and L. Cheng
    Quantum Computational AI, 2026

2025

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    Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems
    S. Dai , N. Saurabh , Q. Wang , J. Nian , S. Kan , Y. Mao , and L. Cheng
    IEEE Systems Journal, 2025
  2. DRL-Cloud-Review.png
    Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
    Y. Gu , Z. Liu , S. Dai , C. Liu , Y. Wang , S. Wang , G. Theodoropoulos , and L. Cheng
    arXiv preprint arXiv:2501.01007, 2025

2024

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    MARP: A Cooperative Multi-Agent DRL System for Connected Autonomous Vehicle Platooning
    S. Dai , S. Li , H. Tang , X. Ning , F. Fang , Y. Fu , Q. Wang , and L. Cheng
    IEEE Internet of Things Journal, 2024
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    Approximate Data Mapping in Refresh-Free DRAM for Energy-Efficient Computing in Modern Mobile Systems
    S. Li , H. Jin , Y. Gao , Y. Wang , S. Dai , Y. Xu , and L. Cheng
    Computer Communications, 2024

2023

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    Deep Reinforcement Learning for Efficient IoT Data Compression in Smart Railroad Management
    X. Chen , Q. Yu , S. Dai , P. Sun , H. Tang , and L. Cheng
    IEEE Internet of Things Journal, 2023