Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the in-exact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and their second moments. The developed algorithm, FRLPGiA, has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.


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    Title :

    Federated Reinforcement Learning for Resource Allocation in V2X Networks


    Contributors:


    Publication date :

    2024-06-24


    Size :

    485250 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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