The application of deep reinforcement learning (DRL) techniques in intelligent transportation systems garners significant attention. In this field, reward function design is a crucial factor for DRL performance. Current research predominantly relies on a trial-and-error approach for designing reward functions, lacking mathematical support and necessitating extensive empirical experimentation. Our research uses vehicle velocity control as a case study, build training and test sets, and develop a DRL framework for speed control. This framework examines both single-objective and multi-objective optimization in reward function designs. In single-objective optimization, we introduce “expected optimal velocity” as an optimization objective and analyze how different reward functions affect performance, providing a mathematical perspective on optimizing reward functions. In multi-objective optimization, we propose a reward function design paradigm and validate its effectiveness. Our findings offer a versatile framework and theoretical guidance for developing and optimizing reward functions in DRL, particularly for intelligent transportation systems.


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    Title :

    Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms


    Additional title:

    Y. HE ET AL.
    TRANSPORTATION LETTERS


    Contributors:
    He, Yixu (author) / Liu, Yang (author) / Yang, Lan (author) / Qu, Xiaobo (author)

    Published in:

    Transportation Letters ; 16 , 10 ; 1338-1352


    Publication date :

    2024-11-25


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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