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Title	Hybrid Reinforcement Learning For Data Stream Freshness In Autonomous Vehicle Networks
ID_Doc	29813
Authors	Ebrahimi D.; Ghosh P.; Alzhouri F.; De Oliveira T.E.A.
Year	2024
Published	Proceedings - IEEE Global Communications Conference, GLOBECOM
DOI	http://dx.doi.org/10.1109/GLOBECOM52923.2024.10901707
Abstract	Autonomous vehicles (AVs) are poised to become integral components of intelligent transportation systems, particularly within the framework of future smart cities. Traditional performance metrics such as throughput and latency fall short in adequately addressing the temporal relevance and freshness of data in critical applications such as autonomous driving and accident prevention. Consequently, this paper delves into the challenge of reducing the Age of Information (AoI) for disseminating data streams within AV-assisted vehicular networks. Given the dynamic nature of the environment, the problem is formulated as a Markov decision process and tackled using Q-learning and DDQN, both prominent reinforcement learning (RL) algorithms. Additionally, a heuristic approach is introduced to augment the performance of the RL algorithms, expediting environmental learning convergence. The numerical findings underscore the effectiveness of the proposed methodologies in minimizing the aggregate AoI across all data streams. © 2024 IEEE.
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