Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles
This paper proposes the application of split computing paradigms for deep reinforcement learning through distributed computation between Connected Autonomous Vehicles (CAVs) and edge servers. While this approach has been explored in computer vision, it remains largely unexplored for reinforcement le...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-06-01
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| Series: | Acta Electrotechnica et Informatica |
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| Online Access: | https://doi.org/10.2478/aei-2025-0008 |
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| author | Rauch Robert Gazda Juraj |
| author_facet | Rauch Robert Gazda Juraj |
| author_sort | Rauch Robert |
| collection | DOAJ |
| description | This paper proposes the application of split computing paradigms for deep reinforcement learning through distributed computation between Connected Autonomous Vehicles (CAVs) and edge servers. While this approach has been explored in computer vision, it remains largely unexplored for reinforcement learning scenarios. We introduce a novel autoencoder trained directly through Deep Q-Network (DQN) rewards, wherein we optimize autoencoder layers using the DQN reward function while maintaining all other layers frozen. Our experimental results demonstrate that the proposed approach outperforms baseline methods by reducing data offloading requirements to the edge server by up to 98.7%. Additionally, this methodology not only decreases the data transmission burden but also achieves comparable rewards. In certain configurations, it even enhancing performance by up to 9.65%. The primary objective of this research is to reduce latency in deep reinforcement learning tasks for autonomous vehicles. In this regard, proposed approach achieves up to 66.5% improvement in latency reduction compared to baseline methods. These findings indicate that partial offloading through split computing offers significant benefits over both full offloading and complete on-device computation strategies for CAVs. |
| format | Article |
| id | doaj-art-e5f261b0a78b45daa01b2e71a5d0fa4d |
| institution | OA Journals |
| issn | 1338-3957 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Acta Electrotechnica et Informatica |
| spelling | doaj-art-e5f261b0a78b45daa01b2e71a5d0fa4d2025-08-20T02:34:16ZengSciendoActa Electrotechnica et Informatica1338-39572025-06-01252212910.2478/aei-2025-0008Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous VehiclesRauch Robert0Gazda Juraj11Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 31751Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175This paper proposes the application of split computing paradigms for deep reinforcement learning through distributed computation between Connected Autonomous Vehicles (CAVs) and edge servers. While this approach has been explored in computer vision, it remains largely unexplored for reinforcement learning scenarios. We introduce a novel autoencoder trained directly through Deep Q-Network (DQN) rewards, wherein we optimize autoencoder layers using the DQN reward function while maintaining all other layers frozen. Our experimental results demonstrate that the proposed approach outperforms baseline methods by reducing data offloading requirements to the edge server by up to 98.7%. Additionally, this methodology not only decreases the data transmission burden but also achieves comparable rewards. In certain configurations, it even enhancing performance by up to 9.65%. The primary objective of this research is to reduce latency in deep reinforcement learning tasks for autonomous vehicles. In this regard, proposed approach achieves up to 66.5% improvement in latency reduction compared to baseline methods. These findings indicate that partial offloading through split computing offers significant benefits over both full offloading and complete on-device computation strategies for CAVs.https://doi.org/10.2478/aei-2025-0008connected autonomous vehiclescontrol theorydeep reinforcement learningedge computingsplit computing |
| spellingShingle | Rauch Robert Gazda Juraj Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles Acta Electrotechnica et Informatica connected autonomous vehicles control theory deep reinforcement learning edge computing split computing |
| title | Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles |
| title_full | Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles |
| title_fullStr | Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles |
| title_full_unstemmed | Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles |
| title_short | Distributed Deep Reinforcement Learning Via Split Computing For Connected Autonomous Vehicles |
| title_sort | distributed deep reinforcement learning via split computing for connected autonomous vehicles |
| topic | connected autonomous vehicles control theory deep reinforcement learning edge computing split computing |
| url | https://doi.org/10.2478/aei-2025-0008 |
| work_keys_str_mv | AT rauchrobert distributeddeepreinforcementlearningviasplitcomputingforconnectedautonomousvehicles AT gazdajuraj distributeddeepreinforcementlearningviasplitcomputingforconnectedautonomousvehicles |