The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algorithms are commonly applied to optimize ITS applications such as resource management and offloading, most RL algorithms focus on single objectives. In many situations, converting a multi-objective problem into a single-objective one is impossible, intractable or insufficient, making such RL algorithms inapplicable. We propose a multi-objective, multi-agent reinforcement learning (MARL) algorithm with high learning efficiency and low computational requirements, which automatically triggers adaptive few-shot learning in a dynamic, distributed and noisy environment with sparse and delayed reward. We test our algorithm in an ITS environment with edge cloud computing. Empirical results show that the algorithm is quick to adapt to new environments and performs better in all individual and system metrics compared to the state-of-the-art benchmark. Our algorithm also addresses various practical concerns with its modularized and asynchronous online training method. In addition to the cloud simulation, we test our algorithm on a single-board computer and show that it can make inference in 6 milliseconds.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning


    Contributors:
    Tan, Jing (author) / Khalili, Ramin (author) / Karl, Holger (author)


    Publication date :

    2024-09-01


    Size :

    11420991 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Adaptive Modulation Using Multi-Objective Reinforcement Learning for LEO Satellites

    Pasquevich, Felipe / Ramirez, Adrian Francisco / Ayarde, Juan Martin et al. | IEEE | 2021


    Multi-Objective Adaptive Cruise Control via Deep Reinforcement Learning

    Zhang, Yourong / Lin, Li / Song, Yizhou et al. | British Library Conference Proceedings | 2022


    Multi-Objective Adaptive Cruise Control via Deep Reinforcement Learning

    Zhang, Yourong / Lin, Li / Song, Yizhou et al. | British Library Conference Proceedings | 2022


    Multi-Objective Adaptive Cruise Control via Deep Reinforcement Learning

    Zhang, Yourong / Lin, Li / Song, Yizhou et al. | British Library Conference Proceedings | 2022


    Multi-Objective Adaptive Cruise Control via Deep Reinforcement Learning

    Huang, Kaisheng / Lin, Li / Song, Yizhou et al. | SAE Technical Papers | 2022