AVDDPG: Federated reinforcement learning applied to autonomous platoon control. (arXiv:2207.03484v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2207.03484

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