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Since 2016 federated learning (FL) has been an evolving topic of discussionin the artificial intelligence (AI) research community. Applications of FL ledto the development and study of federated reinforcement learning (FRL). Fewworks exist on the topic of FRL applied to autonomous vehicle (AV) platoons. Inaddition, most FRL works choose a single aggregation method (usually weight orgradient aggregation). We explore FRL's effectiveness as a means to improve AVplatooning by designing and implementing an FRL framework atop a custom AVplatoon environment. The application of FRL in AV platooning is studied undertwo scenarios: (1) Inter-platoon FRL (Inter-FRL) where FRL is applied to AVsacross different platoons; (2) Intra-platoon FRL (Intra-FRL) where FRL isapplied to AVs within a single platoon. Both Inter-FRL and Intra-FRL areapplied to a custom AV platooning environment using both gradient and weightaggregation to observe the performance effects FRL can have on AV platoonsrelative to an AV platooning environment trained without FRL. It is concludedthat Intra-FRL using weight aggregation (Intra-FRLWA) provides the bestperformance for controlling an AV platoon. In addition, we found that weightaggregation in FRL for AV platooning provides increases in performance relativeto gradient aggregation. Finally, a performance analysis is conducted forIntra-FRLWA versus a platooning environment without FRL for platoons of length3, 4 and 5 vehicles. It is concluded that Intra-FRLWA largely out-performs theplatooning environment that is trained without FRL.

AVDDPG: Federated reinforcement learning applied to autonomous platoon control