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AVDDPG: Federated reinforcement learning applied to autonomous platoon control

arXiv 2207.03484 ↗First posted 5 Jul 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Since 2016 federated learning (FL) has been an evolving topic of discussion in the artificial intelligence (AI) research community. Applications of FL led to the development and study of federated reinforcement learning (FRL). Few works exist on the topic of FRL applied to autonomous vehicle (AV) platoons. In addition, most FRL works choose a single aggregation method (usually weight or gradient aggregation). We explore FRL's effectiveness as a means to improve AV platooning by designing and implementing an FRL framework atop a custom AV platoon environment. The application of FRL in AV platooning is studied under two scenarios: (1) Inter-platoon FRL (Inter-FRL) where FRL is applied to AVs across different platoons; (2) Intra-platoon FRL (Intra-FRL) where FRL is applied to AVs within a single platoon. Both Inter-FRL and Intra-FRL are applied to a custom AV platooning environment using both gradient and weight aggregation to observe the performance effects FRL can have on AV platoons relative to an AV platooning environment trained without FRL. It is concluded that Intra-FRL using weight aggregation (Intra-FRLWA) provides the best performance for controlling an AV platoon. In addition, we found that weight aggregation in FRL for AV platooning provides increases in performance relative to gradient aggregation. Finally, a performance analysis is conducted for Intra-FRLWA versus a platooning environment without FRL for platoons of length 3, 4 and 5 vehicles. It is concluded that Intra-FRLWA largely out-performs the platooning environment that is trained without FRL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 5 Jul 2022.

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References · 17

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5 more
  1. 2018Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environmentcited by 16
  2. 2020Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communicationscited by 14
  3. 2019Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learningcited by 10
  4. 2019Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehiclescited by 5
  5. 2021Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Controlcited by 5

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