Reinforcement Learning with Distributed MPC for Fuel-Efficient Platoon Control with Discrete Gear Transitions

作者：Samuel Mallick, Gianpietro Battocletti, Dimitris Boskos, A. Dabiri, B. D. Schutter · 发表于：arXiv.org · 年份：2026 · DOI：10.48550/arxiv.2601.18294 · 被引用次数：1 · 研究领域：Computer Science、Engineering

Cooperative control of groups of autonomous vehicles (AVs), i.e., platoons, is a promising direction to improving the efficiency of autonomous transportation systems. In this context, distributed co-optimization of both vehicle speed and gear position can offer benefits for fuel-efficient driving. To this end, model predictive control (MPC) is a popular approach, optimizing the speed and gear-shift schedule while explicitly considering the vehicles'dynamics over a prediction window. However, optimization over both the vehicles'continuous dynamics and discrete gear positions is computationally intensive, and may require overly long sample times or high-end hardware for real-time implementation. This work proposes a reinforcement learning (RL)-based distributed MPC approach to address this issue. For each vehicle in the platoon, a policy is trained to select and fix the gear positions across the prediction window of a local MPC controller, leaving a significantly simpler continuous optimization problem to be solved as part of a distributed MPC scheme. In order to reduce the computational cost of training and facilitate the scalability of the proposed approach to large platoons, the policies are parameterized such that the emergent multi-agent RL problem can be decoupled into single-agent learning tasks. In addition, a recurrent neural-network (RNN) architecture is proposed for the gear selection policy, such that the learning is scalable even as the number of possible gear-shif...