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As advanced driver-assistant systems move towards higher automation levels, the industry now frequently employs data-driven techniques like Reinforcement Learning over rule-based systems. While the current literature on this topic contains analyses of the multi-objective and multi-agent settings, the combination of these two aspects is largely unexplored. This paper proposes a Q-learning-based Reinforcement Learning algorithm combining the multi-objective and multi-agent aspects. The algorithm was tested on a highway traffic environment, displaying its ability to produce policies that consider user preferences, exhibit social behavior, and adhere to basic traffic regulations to a certain extent. As such, the proposed approach is deemed to provide a promising foundation to be used as a baseline algorithm for future research advancements.

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Multi-objective Multi-agent Reinforcement Learning for Autonomous Driving in Mixed-Traffic Environments

  • 会议论文

  • 发表日期：

    2025年

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  • Franz Herm,
  • Atanu Mazumdar,
  • Tinkle Chugh

摘要

As advanced driver-assistant systems move towards higher automation levels, the industry now frequently employs data-driven techniques like Reinforcement Learning over rule-based systems. While the current literature on this topic contains analyses of the multi-objective and multi-agent settings, the combination of these two aspects is largely unexplored. This paper proposes a Q-learning-based Reinforcement Learning algorithm combining the multi-objective and multi-agent aspects. The algorithm was tested on a highway traffic environment, displaying its ability to produce policies that consider user preferences, exhibit social behavior, and adhere to basic traffic regulations to a certain extent. As such, the proposed approach is deemed to provide a promising foundation to be used as a baseline algorithm for future research advancements.