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This research addresses the packet delivery in vehicular communication systems using various reinforcement learning algorithms. The improvement process is executed using Q-learning, SARSA, DQN, and DDQN algorithms. The comparative analysis of algorithmic efficiency aims to identify the most effective reinforcement learning approach for enhancing communication reliability in vehicular networks, contributing to the advancement of intelligent transportation systems. The objectives of the research are to design and develop a simulation environment for VANETs and evaluate the performance of the various reinforcement learning models in improving network performance. The goals of the research are to demonstrate the effectiveness of reinforcement learning in improving network performance in VANETs, compare the performance of various reinforcement learning models in improving network performance, and provide a framework for developing more advanced algorithms for VANETs in the future. By designing a simulation environment tailored for VANETs, we evaluated different reinforcement learning models and found that they notably increase the packet delivery ratio (PDR), thus improving network performance and facilitating the successful delivery of a greater number of packets.

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Reinventing Urban Mobility with Reinforcement Learning in Vehicular Ad-Hoc Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Aayush Anand,
  • Pranav Anne,
  • Rohan Sanjay,
  • Bhuvan Annappa,
  • L. Kamatchi Priya

摘要

This research addresses the packet delivery in vehicular communication systems using various reinforcement learning algorithms. The improvement process is executed using Q-learning, SARSA, DQN, and DDQN algorithms. The comparative analysis of algorithmic efficiency aims to identify the most effective reinforcement learning approach for enhancing communication reliability in vehicular networks, contributing to the advancement of intelligent transportation systems. The objectives of the research are to design and develop a simulation environment for VANETs and evaluate the performance of the various reinforcement learning models in improving network performance. The goals of the research are to demonstrate the effectiveness of reinforcement learning in improving network performance in VANETs, compare the performance of various reinforcement learning models in improving network performance, and provide a framework for developing more advanced algorithms for VANETs in the future. By designing a simulation environment tailored for VANETs, we evaluated different reinforcement learning models and found that they notably increase the packet delivery ratio (PDR), thus improving network performance and facilitating the successful delivery of a greater number of packets.